医药卫生科技论文参考文献格式举例怎么写?临近毕业季,大家都在为自己的论文答辩时刻准备着,想要顺利通过,认真、严谨是必备的态度。对于论文文献格式,很多同学不知如何写,本文为大家提供了200个医药卫生科技论文参考文献格式案例,大家可以参考一下。
[1] Wu E, Wu K, Daneshjou R, et al. How medical AI devices are evaluated:
limitations and recommendations from an analysis of FDA approvals[J]. Nature
Medicine, 2021, 27(4): 582-584.
[2] Ciresan D, Giusti A, Gambardella L, et al. Deep neural networks segment neuronal
membranes in electron microscopy images[J]. Advances in neural information
processing systems, 2012, 25.
[3] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic
segmentation[C]//Proceedings of the IEEE conference on computer vision and
pattern recognition. 2015: 3431-3440.
[4] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical
image segmentation[C]//Medical Image Computing and Computer-Assisted
Intervention–MICCAI 2015: 18th International Conference, Munich, Germany,
October 5-9, 2015, Proceedings, Part III 18. Springer International Publishing,
2015: 234-241.
[5] Huang Q, Sun J, Ding H, et al. Robust liver vessel extraction using 3D U-Net with
variant dice loss function[J]. Computers in biology and medicine, 2018, 101: 153-
162.
[6] Yu W, Fang B, Liu Y, et al. Liver vessels segmentation based on 3d residual UNET[C]//2019 IEEE international conference on image processing (ICIP). IEEE,
2019: 250-254.
[7] 殷晓航, 王永才, 李德英. 基于 U-Net 结构改进的医学影像分割技术综述
[J]. 软件学报, 2020, 32(2): 519-550.
[8] Zhou Z, Rahman Siddiquee M M, Tajbakhsh N, et al. Unet++: A nested u-net
architecture for medical image segmentation[C]//Deep Learning in Medical Image
Analysis and Multimodal Learning for Clinical Decision Support: 4th International
Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in
Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018,
Proceedings 4. Springer International Publishing, 2018: 3-11.
[9] Zhang Z, Fu H, Dai H, et al. Et-net: A generic edge-attention guidance network for
medical image segmentation[C]//Medical Image Computing and Computer
Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen,
China, October 13–17, 2019, Proceedings, Part I 22. Springer International
Publishing, 2019: 442-450.
[10]Chen W, Liu B, Peng S, et al. S3D-UNet: separable 3D U-Net for brain tumor
segmentation[C]//Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic
Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction
with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers,
Part II 4. Springer International Publishing, 2019: 358-368.
[11]Zhang Z, Wu C, Coleman S, et al. DENSE-INception U-net for medical image
segmentation[J]. Computer methods and programs in biomedicine, 2020, 192:
105395.
[12]Ibtehaz N, Rahman M S. MultiResUNet: Rethinking the U-Net architecture for
multimodal biomedical image segmentation[J]. Neural networks, 2020, 121: 74-87.
[13]Liu H, Shen X, Shang F, et al. CU-Net: Cascaded U-Net with loss weighted
sampling for brain tumor segmentation[C]//Multimodal Brain Image Analysis and
Mathematical Foundations of Computational Anatomy: 4th International
Workshop, MBIA 2019, and 7th International Workshop, MFCA 2019, Held in
Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings
4. Springer International Publishing, 2019: 102-111.
[14]Baldeon-Calisto M, Lai-Yuen S K. AdaResU-Net: Multiobjective adaptive
convolutional neural network for medical image segmentation[J]. Neurocomputing,
2020, 392: 325-340.
[15]Abraham N, Khan N M. A novel focal tversky loss function with improved
attention u-net for lesion segmentation[C]//2019 IEEE 16th international
symposium on biomedical imaging (ISBI 2019). IEEE, 2019: 683-687.
[16]Zhao H, Sun N. Improved U-net model for nerve segmentation[C]//Image and
Graphics: 9th International Conference, ICIG 2017, Shanghai, China, September
13-15, 2017, Revised Selected Papers, Part II 9. Springer International Publishing,
2017: 496-504.
[17]Zhang Q, Cui Z, Niu X, et al. Image segmentation with pyramid dilated
convolution based on ResNet and U-Net[C]//Neural Information Processing: 24th
International Conference, ICONIP 2017, Guangzhou, China, November 14-18,
2017, Proceedings, Part II 24. Springer International Publishing, 2017: 364-372.
[18]Frid-Adar M, Ben-Cohen A, Amer R, et al. Improving the segmentation of
anatomical structures in chest radiographs using u-net with an imagenet pre-trained
encoder[C]//Image Analysis for Moving Organ, Breast, and Thoracic Images:
Third International Workshop, RAMBO 2018, Fourth International Workshop, BIA
2018, and First International Workshop, TIA 2018, Held in Conjunction with
MICCAI 2018, Granada, Spain, September 16 and 20, 2018, Proceedings 3.
Springer International Publishing, 2018: 159-168.
[19]Waiker D, Baghel P D, Varma K R, et al. Effective semantic segmentation of lung
x-ray images using u-net architecture[C]//2020 Fourth International Conference on
Computing Methodologies and Communication (ICCMC). IEEE, 2020: 603-607.
[20]Orlando J I, Seeböck P, Bogunović H, et al. U2-net: A bayesian u-net model with
epistemic uncertainty feedback for photoreceptor layer segmentation in
pathological oct scans[C]//2019 IEEE 16th International Symposium on
Biomedical Imaging (ISBI 2019). IEEE, 2019: 1441-1445.
[21]Asgari R, Waldstein S, Schlanitz F, et al. U-Net with spatial pyramid pooling for
drusen segmentation in optical coherence tomography[C]//Ophthalmic Medical
Image Analysis: 6th International Workshop, OMIA 2019, Held in Conjunction
with MICCAI 2019, Shenzhen, China, October 17, Proceedings 6. Springer
International Publishing, 2019: 77-85.
[22]Zhong Z, Kim Y, Zhou L, et al. 3D fully convolutional networks for cosegmentation of tumors on PET-CT images[C]//2018 IEEE 15th International
Symposium on Biomedical Imaging (ISBI 2018). IEEE, 2018: 228-231.
[23]Wang H, Wang Z, Wang J, et al. ICA-Unet: An improved U-net network for brown
adipose tissue segmentation[J]. Journal of Innovative Optical Health Sciences,
2022, 15(03): 2250018.
[24]Otsu N. A threshold selection method from gray-level histograms[J]. IEEE
transactions on systems, man, and cybernetics, 1979, 9(1): 62-66.
[25]Nock R, Nielsen F. Statistical region merging[J]. IEEE Transactions on pattern
analysis and machine intelligence, 2004, 26(11): 1452-1458.
[26]Dhanachandra N, Manglem K, Chanu Y J. Image segmentation using K-means
clustering algorithm and subtractive clustering algorithm[J]. Procedia Computer
Science, 2015, 54: 764-771.
[27]Najman L, Schmitt M. Watershed of a continuous function[J]. Signal Processing,
1994, 38(1): 99-112.
[28]Kass M, Witkin A, Terzopoulos D. Snakes: Active contour models[J]. International
journal of computer vision, 1988, 1(4): 321-331.
[29]Boykov Y, Veksler O, Zabih R. Fast approximate energy minimization via graph
cuts[J]. IEEE Transactions on pattern analysis and machine intelligence, 2001,
23(11): 1222-1239.
[30]Plath N, Toussaint M, Nakajima S. Multi-class image segmentation using
conditional random fields and global classification[C]//Proceedings of the 26th
annual international conference on machine learning. 2009: 817-824.
[31]Starck J L, Elad M, Donoho D L. Image decomposition via the combination of
sparse representations and a variational approach[J]. IEEE transactions on image
processing, 2005, 14(10): 1570-1582.
[32]Minaee S, Wang Y. An ADMM approach to masked signal decomposition using
subspace representation[J]. IEEE Transactions on Image Processing, 2019, 28(7):
3192-3204.
[33]Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]//Proceedings
of the IEEE conference on computer vision and pattern recognition. 2015: 1-9.
[34]He K, Zhang X, Ren S, et al. Deep residual learning for image
recognition[C]//Proceedings of the IEEE conference on computer vision and
pattern recognition. 2016: 770-778.
[35]Gibson E, Giganti F, Hu Y, et al. Towards image-guided pancreas and biliary
endoscopy: automatic multi-organ segmentation on abdominal CT with dense
dilated networks[C]//Medical Image Computing and Computer Assisted
Intervention− MICCAI 2017: 20th International Conference, Quebec City, QC,
Canada, September 11-13, 2017, Proceedings, Part I 20. Springer International
Publishing, 2017: 728-736.
[36]Zhou X, Takayama R, Wang S, et al. Deep learning of the sectional appearances
of 3D CT images for anatomical structure segmentation based on an FCN voting
method[J]. Medical physics, 2017, 44(10): 5221-5233.
[37]Hu P, Wu F, Peng J, et al. Automatic abdominal multi-organ segmentation using
deep convolutional neural network and time-implicit level sets[J]. International
journal of computer assisted radiology and surgery, 2017, 12: 399-411.
[38]蔡林沁, 易文渊, 黄宇婷, 等. 面向脑胶质瘤影像分析的混合现实技术[J].
软件学报, 2022, 33(9): 3347-3369.
[39]Christ P F, Elshaer M E A, Ettlinger F, et al. Automatic liver and lesion
segmentation in CT using cascaded fully convolutional neural networks and 3D
conditional random fields[C]//Medical Image Computing and Computer-Assisted
Intervention–MICCAI 2016: 19th International Conference, Athens, Greece,
October 17-21, 2016, Proceedings, Part II 19. Springer International Publishing,
2016: 415-423.
[40]Kamnitsas K, Ledig C, Newcombe V F J, et al. Efficient multi-scale 3D CNN with
fully connected CRF for accurate brain lesion segmentation[J]. Medical image
analysis, 2017, 36: 61-78.
[41]Zhang H, Kyaw Z, Yu J, et al. Ppr-fcn: Weakly supervised visual relation detection
via parallel pairwise r-fcn[C]//Proceedings of the IEEE international conference on
computer vision. 2017: 4233-4241.
[42]Wang J, MacKenzie J D, Ramachandran R, et al. A deep learning approach for
semantic segmentation in histology tissue images[C]//Medical Image Computing
and Computer-Assisted Intervention–MICCAI 2016: 19th International
Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19.
Springer International Publishing, 2016: 176-184.
[43]Yu L, Chen H, Dou Q, et al. Automated melanoma recognition in dermoscopy
images via very deep residual networks[J]. IEEE transactions on medical imaging,
2016, 36(4): 994-1004.
[44]Zeng G, Zheng G. Multi-stream 3D FCN with multi-scale deep supervision for
multi-modality isointense infant brain MR image segmentation[C]//2018 IEEE
15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE, 2018:
136-140.
[45]Zhang W, Li R, Deng H, et al. Deep convolutional neural networks for multimodality isointense infant brain image segmentation[J]. NeuroImage, 2015, 108:
214-224.
[46]Liu L, Cheng J, Quan Q, et al. A survey on U-shaped networks in medical image
segmentations[J]. Neurocomputing, 2020, 409: 244-258.
[47]Li C, Tan Y, Chen W, et al. ANU-Net: Attention-based nested U-Net to exploit full
resolution features for medical image segmentation[J]. Computers & Graphics,
2020, 90: 11-20.
[48]Jha D, Riegler M A, Johansen D, et al. Doubleu-net: A deep convolutional neural
network for medical image segmentation[C]//2020 IEEE 33rd International
symposium on computer-based medical systems (CBMS). IEEE, 2020: 558-564.
[49]Li J, Chen C, Wang L. Fusion algorithm of multi-spectral images based on dualtree complex wavelet transform and frequency-domain U-Net[J]. Journal of
Biomedical Engineering Research, 2020, 39(2): 145-150.
[50]Zhang T, Kang B, Meng X, et al. U-Net based intracranial hemorrhage
recognition[J]. Journal of Beijing University of Posts and Telecommunications,
2020, 43(3): 92.
[51]Yang Y, Feng C, Wang R. Automatic segmentation model combining U-Net and
level set method for medical images[J]. Expert Systems with Applications, 2020,
153: 113419.
[52]Zhang H, Zhu H, Ling X. Polar coordinate sampling-based segmentation of
overlapping cervical cells using attention U-Net and random walk[J].
Neurocomputing, 2020, 383: 212-223.
[53]Liu Z, Song Y Q, Sheng V S, et al. Liver CT sequence segmentation based with
improved U-Net and graph cut[J]. Expert Systems with Applications, 2019, 126:
54-63.
[54]Man Y, Huang Y, Feng J, et al. Deep Q learning driven CT pancreas segmentation
with geometry-aware U-Net[J]. IEEE transactions on medical imaging, 2019, 38(8):
1971-1980.
[55]Milletari F, Navab N, Ahmadi S A. V-net: Fully convolutional neural networks for
volumetric medical image segmentation[C]//2016 fourth international conference
on 3D vision (3DV). Ieee, 2016: 565-571.
[56]Çiçek Ö, Abdulkadir A, Lienkamp S S, et al. 3D U-Net: learning dense volumetric
segmentation from sparse annotation[C]//Medical Image Computing and
Computer-Assisted Intervention–MICCAI 2016: 19th International Conference,
Athens, Greece, October 17-21, 2016, Proceedings, Part II 19. Springer
International Publishing, 2016: 424-432.
[57]Kleesiek J, Urban G, Hubert A, et al. Deep MRI brain extraction: A 3D
convolutional neural network for skull stripping[J]. NeuroImage, 2016, 129: 460-
469.
[58]Xie S, Tu Z. Holistically-nested edge detection[C]//Proceedings of the IEEE
international conference on computer vision. 2015: 1395-1403.
[59]Zeng G, Yang X, Li J, et al. 3D U-net with multi-level deep supervision: fully
automatic segmentation of proximal femur in 3D MR images[C]//Machine
Learning in Medical Imaging: 8th International Workshop, MLMI 2017, Held in
Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 10, 2017,
Proceedings 8. Springer International Publishing, 2017: 274-282.
[60]Li X, Chen H, Qi X, et al. H-DenseUNet: hybrid densely connected UNet for liver
and tumor segmentation from CT volumes[J]. IEEE transactions on medical
imaging, 2018, 37(12): 2663-2674.
[61]Huang H, Lin L, Tong R, et al. Unet 3+: A full-scale connected unet for medical
image segmentation[C]//ICASSP 2020-2020 IEEE international conference on
acoustics, speech and signal processing (ICASSP). IEEE, 2020: 1055-1059.
[62]Xiang T, Zhang C, Liu D, et al. BiO-Net: learning recurrent bi-directional
connections for encoder-decoder architecture[C]//Medical Image Computing and
Computer Assisted Intervention–MICCAI 2020: 23rd International Conference,
Lima, Peru, October 4–8, 2020, Proceedings, Part I 23. Springer International
Publishing, 2020: 74-84.
[63]Oktay O, Schlemper J, Folgoc L L, et al. Attention u-net: Learning where to look [63]Oktay O, Schlemper J, Folgoc L L, et al. Attention u-net: Learning where to look
for the pancreas[J]. arXiv preprint arXiv:1804.03999, 2018.
[64]Jin Q, Meng Z, Sun C, et al. RA-UNet: A hybrid deep attention-aware network to
extract liver and tumor in CT scans[J]. Frontiers in Bioengineering and
Biotechnology, 2020, 8: 605132.
[65]Li C, Tan Y, Chen W, et al. Attention unet++: A nested attention-aware u-net for
liver ct image segmentation[C]//2020 IEEE international conference on image
processing (ICIP). IEEE, 2020: 345-349.
[66]毕秀丽, 陆猛, 肖斌, 等. 基于双解码 U 型卷积神经网络的胰腺分割[J].
软件学报, 2022, 33(5): 1947-1958.
[67]Li H, Fang J, Liu S, et al. Cr-unet: A composite network for ovary and follicle
segmentation in ultrasound images[J]. IEEE journal of biomedical and health
informatics, 2019, 24(4): 974-983.
[68]Lachinov D, Seeböck P, Mai J, et al. Projective skip-connections for segmentation
along a subset of dimensions in retinal OCT[C]//Medical Image Computing and
Computer Assisted Intervention–MICCAI 2021: 24th International Conference,
Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24.
Springer International Publishing, 2021: 431-441.
[69]Azad R, Asadi-Aghbolaghi M, Fathy M, et al. Bi-directional ConvLSTM U-Net
with densley connected convolutions[C]//Proceedings of the IEEE/CVF
international conference on computer vision workshops. 2019: 0-0.
[70]Drozdzal M, Vorontsov E, Chartrand G, et al. The importance of skip connections
in biomedical image segmentation[C]//International Workshop on Deep Learning
in Medical Image Analysis, International Workshop on Large-Scale Annotation of
Biomedical Data and Expert Label Synthesis. Springer, Cham, 2016: 179-187.
[71]Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for
computer vision[C]//Proceedings of the IEEE conference on computer vision and
pattern recognition. 2016: 2818-2826.
[72]Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional
networks[C]//Proceedings of the IEEE conference on computer vision and pattern
recognition. 2017: 4700-4708.
[73]Karaali A, Dahyot R, Sexton D J. DR-VNet: retinal vessel segmentation via dense
residual UNet[C]//International Conference on Pattern Recognition and Artificial
Intelligence. Cham: Springer International Publishing, 2022: 198-210.
[74]Hai J, Qiao K, Chen J, et al. Fully convolutional densenet with multiscale context
for automated breast tumor segmentation[J]. Journal of healthcare engineering,
2019, 2019.
[75]Wang W, Chen C, Ding M, et al. Transbts: Multimodal brain tumor segmentation
using transformer[C]//Medical Image Computing and Computer Assisted
Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France,
September 27–October 1, 2021, Proceedings, Part I 24. Springer International
Publishing, 2021: 109-119.
[76]Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words:
Transformers for image recognition at scale[J]. arXiv preprint arXiv:2010.11929,
2020.
[77]Chen J, Lu Y, Yu Q, et al. Transunet: Transformers make strong encoders for
medical image segmentation[J]. arXiv preprint arXiv:2102.04306, 2021.
[78]Hatamizadeh A, Tang Y, Nath V, et al. Unetr: Transformers for 3d medical image
segmentation[C]//Proceedings of the IEEE/CVF winter conference on applications
of computer vision. 2022: 574-584.
[79]Wang H, Xie S, Lin L, et al. Mixed transformer u-net for medical image
segmentation[C]//ICASSP 2022-2022 IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP). IEEE, 2022: 2390-2394.
[80]Azad R, Heidari M, Wu Y, et al. Contextual attention network: Transformer meets
u-net[C]//International Workshop on Machine Learning in Medical Imaging. Cham:
Springer Nature Switzerland, 2022: 377-386.
[81]Wang H, Cao P, Wang J, et al. Uctransnet: rethinking the skip connections in u-net
from a channel-wise perspective with transformer[C]//Proceedings of the AAAI
conference on artificial intelligence. 2022, 36(3): 2441-2449.
[82]Cao H, Wang Y, Chen J, et al. Swin-unet: Unet-like pure transformer for medical
image segmentation[C]//European conference on computer vision. Cham: Springer
Nature Switzerland, 2022: 205-218.
[83]Huang X, Deng Z, Li D, et al. Missformer: An effective transformer for 2d medical
image segmentation[J]. IEEE Transactions on Medical Imaging, 2022.
[84]Yan X, Tang H, Sun S, et al. After-unet: Axial fusion transformer unet for medical
image segmentation[C]//Proceedings of the IEEE/CVF winter conference on
applications of computer vision. 2022: 3971-3981.
[85]Valanarasu J M J, Oza P, Hacihaliloglu I, et al. Medical transformer: Gated axialattention for medical image segmentation[C]//Medical Image Computing and
Computer Assisted Intervention–MICCAI 2021: 24th International Conference,
Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24.
Springer International Publishing, 2021: 36-46.
[86]Zhang C, Wang L, Cheng S, et al. SwinSUNet: Pure transformer network for
remote sensing image change detection[J]. IEEE Transactions on Geoscience and
Remote Sensing, 2022, 60: 1-13.
[87]Burt P J, Adelson E H. The Laplacian pyramid as a compact image
code[M]//Readings in computer vision. Morgan Kaufmann, 1987: 671-679.
[88]Lin T Y, Dollár P, Girshick R, et al. Feature pyramid networks for object
detection[C]//Proceedings of the IEEE conference on computer vision and pattern
recognition. 2017: 2117-2125.
[89]Wang W, Xie E, Li X, et al. Pyramid vision transformer: A versatile backbone for
dense prediction without convolutions[C]//Proceedings of the IEEE/CVF
international conference on computer vision. 2021: 568-578.
[90]Liu Z, Lin Y, Cao Y, et al. Swin transformer: Hierarchical vision transformer using
shifted windows[C]//Proceedings of the IEEE/CVF international conference on
computer vision. 2021: 10012-10022.
[91]Wu H, Xiao B, Codella N, et al. Cvt: Introducing convolutions to vision
transformers[C]//Proceedings of the IEEE/CVF international conference on
computer vision. 2021: 22-31.
[92]Wang W, Xie E, Li X, et al. Pvt v2: Improved baselines with pyramid vision
transformer[J]. Computational Visual Media, 2022, 8(3): 415-424.
[93]Xu W, Xu Y, Chang T, et al. Co-scale conv-attentional image
transformers[C]//Proceedings of the IEEE/CVF International Conference on
Computer Vision. 2021: 9981-9990.
[94]Xie E, Wang W, Yu Z, et al. SegFormer: Simple and efficient design for semantic
segmentation with transformers[J]. Advances in Neural Information Processing
Systems, 2021, 34: 12077-12090.
[95]Chen C F R, Fan Q, Panda R. Crossvit: Cross-attention multi-scale vision
transformer for image classification[C]//Proceedings of the IEEE/CVF
international conference on computer vision. 2021: 357-366.
[96]Codella N C F, Gutman D, Celebi M E, et al. Skin lesion analysis toward
melanoma detection: A challenge at the 2017 international symposium on
biomedical imaging (isbi), hosted by the international skin imaging collaboration
(isic)[C]//2018 IEEE 15th international symposium on biomedical imaging (ISBI
2018). IEEE, 2018: 168-172.
[97]Bernal J, Sánchez F J, Fernández-Esparrach G, et al. WM-DOVA maps for
accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from
physicians[J]. Computerized medical imaging and graphics, 2015, 43: 99-111.
[98]Caicedo J C, Goodman A, Karhohs K W, et al. Nucleus segmentation across
imaging experiments: the 2018 Data Science Bowl[J]. Nature methods, 2019,
16(12): 1247-1253.
[99]Synapse. Synapse: syn3193805. Retrieved from
https://www.synapse.org/#!Synapse:syn3193805/wiki/217789
[100]ACDC Challenge. ACDC Challenge. Retrieved from https://www.creatis.insalyon.fr/Challenge/acdc/
[101]Everingham M, Van Gool L, Williams C K I, et al. The pascal visual object
classes (voc) challenge[J]. International journal of computer vision, 2010, 88: 303-
338.
[102]Hariharan B, Arbeláez P, Bourdev L, et al. Semantic contours from inverse
detectors[C]//2011 international conference on computer vision. IEEE, 2011: 991-
998.
[103]Cordts M, Omran M, Ramos S, et al. The cityscapes dataset for semantic urban
scene understanding[C]//Proceedings of the IEEE conference on computer vision
and pattern recognition. 2016: 3213-3223.
[104]Chen L C, Papandreou G, Kokkinos I, et al. Deeplab: Semantic image
segmentation with deep convolutional nets, atrous convolution, and fully
connected crfs[J]. IEEE transactions on pattern analysis and machine intelligence,
2017, 40(4): 834-848.
[105]Gu Z, Cheng J, Fu H, et al. Ce-net: Context encoder network for 2d medical
image segmentation[J]. IEEE transactions on medical imaging, 2019, 38(10):
2281-2292.
[106]Schlemper J, Oktay O, Schaap M, et al. Attention gated networks: Learning to
leverage salient regions in medical images[J]. Medical image analysis, 2019, 53:
197-207.
[107]Wang X, Girshick R, Gupta A, et al. Non-local neural networks[C]//Proceedings
of the IEEE conference on computer vision and pattern recognition. 2018: 7794-
7803.
[108]孙颖,丁卫平,黄嘉爽,等.RCAR-UNet:基于粗糙通道注意力机制的视网膜血
管分割网络 [J]. 计 算 机 研 究 与 发 展 , 2023,
60(4):15.DOI:10.7544/issn1000-1239.202110735.
[109]Zhao H, Shi J, Qi X, et al. Pyramid scene parsing network[C]//Proceedings of the
IEEE conference on computer vision and pattern recognition. 2017: 2881-2890.
[110]Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[J]. Advances in
neural information processing systems, 2017, 30.
[111]Carion N, Massa F, Synnaeve G, et al. End-to-end object detection with
transformers[C]//European conference on computer vision. Cham: Springer
International Publishing, 2020: 213-229.
[112]田永林, 王雨桐, 王建功, 等. 视觉 Transformer 研究的关键问题: 现
状及展望[J]. 自动化学报, 2022, 48(4): 957-979.
[113]毛琳, 任凤至, 杨大伟, 等. 基于卷积神经网络的全景分割 Transformer
模型[J]. 软件学报, 2022, 34(7): 3408-3421.
[114]Touvron H, Cord M, Douze M, et al. Training data-efficient image transformers
& distillation through attention[C]//International conference on machine learning.
PMLR, 2021: 10347-10357.
[115]Hu H, Zhang Z, Xie Z, et al. Local relation networks for image
recognition[C]//Proceedings of the IEEE/CVF International Conference on
Computer Vision. 2019: 3464-3473.
[116]Ramachandran P, Parmar N, Vaswani A, et al. Stand-alone self-attention in vision
models[J]. Advances in neural information processing systems, 2019, 32.
[117]Tolstikhin I O, Houlsby N, Kolesnikov A, et al. Mlp-mixer: An all-mlp
architecture for vision[J]. Advances in neural information processing systems,
2021, 34: 24261-24272.
[118]Xie S, Girshick R, Dollár P, et al. Aggregated residual transformations for deep
neural networks[C]//Proceedings of the IEEE conference on computer vision and
pattern recognition. 2017: 1492-1500.
[119]Badrinarayanan V, Kendall A, Cipolla R. Segnet: A deep convolutional encoderdecoder architecture for image segmentation[J]. IEEE transactions on pattern
analysis and machine intelligence, 2017, 39(12): 2481-2495.
[120]Lin G, Milan A, Shen C, et al. Refinenet: Multi-path refinement networks for
high-resolution semantic segmentation[C]//Proceedings of the IEEE conference on
computer vision and pattern recognition. 2017: 1925-1934.
[121]Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//Proceedings of the
IEEE conference on computer vision and pattern recognition. 2018: 7132-7141.
[122]Wang F, Jiang M, Qian C, et al. Residual attention network for image
classification[C]//Proceedings of the IEEE conference on computer vision and
pattern recognition. 2017: 3156-3164.
[123]Visin F, Ciccone M, Romero A, et al. Reseg: A recurrent neural network-based
model for semantic segmentation[C]//Proceedings of the IEEE conference on
computer vision and pattern recognition workshops. 2016: 41-48.
[124]Yang M, Yu K, Zhang C, et al. Denseaspp for semantic segmentation in street
scenes[C]//Proceedings of the IEEE conference on computer vision and pattern
recognition. 2018: 3684-3692.
[125]He J, Deng Z, Zhou L, et al. Adaptive pyramid context network for semantic
segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition. 2019: 7519-7528.
[126]Chen L C, Papandreou G, Schroff F, et al. Rethinking atrous convolution for
semantic image segmentation[J]. arXiv preprint arXiv:1706.05587, 2017.
[127]He K, Zhang X, Ren S, et al. Identity mappings in deep residual
networks[C]//Computer Vision–ECCV 2016: 14th European Conference,
Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14.
Springer International Publishing, 2016: 630-645.
[128]Jha D, Smedsrud P H, Riegler M A, et al. Resunet++: An advanced architecture
for medical image segmentation[C]//2019 IEEE international symposium on
multimedia (ISM). IEEE, 2019: 225-2255.
[129]Jha D, Riegler M A, Johansen D, et al. Doubleu-net: A deep convolutional neural
network for medical image segmentation[C]//2020 IEEE 33rd International
symposium on computer-based medical systems (CBMS). IEEE, 2020: 558-564.
[130]Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by
reducing internal covariate shift[C]//International conference on machine learning.
pmlr, 2015: 448-456.
[131]Zhang Z, Zhang X, Peng C, et al. Exfuse: Enhancing feature fusion for semantic
segmentation[C]//Proceedings of the European conference on computer vision
(ECCV). 2018: 269-284.
[132]Guo M H, Liu Z N, Mu T J, et al. Beyond self-attention: External attention using
two linear layers for visual tasks[J]. IEEE Transactions on Pattern Analysis and
Machine Intelligence, 2022, 45(5): 5436-5447.
[133]Li Z, Yang S, Song G, et al. Hamnet: Conformation-guided molecular
representation with hamiltonian neural networks[J]. arXiv preprint
arXiv:2105.03688, 2021.
[134]Chen L C, Zhu Y, Papandreou G, et al. Encoder-decoder with atrous separable
convolution for semantic image segmentation[C]//Proceedings of the European
conference on computer vision (ECCV). 2018: 801-818.
[135]Chen L C, Papandreou G, Schroff F, et al. Rethinking atrous convolution for
semantic image segmentation[J]. arXiv preprint arXiv:1706.05587, 2017.
[136]Liu C, Chen L C, Schroff F, et al. Auto-deeplab: Hierarchical neural architecture
search for semantic image segmentation[C]//Proceedings of the IEEE/CVF
conference on computer vision and pattern recognition. 2019: 82-92.
[137]Li X, Liu Z, Luo P, et al. Not all pixels are equal: Difficulty-aware semantic
segmentation via deep layer cascade[C]//Proceedings of the IEEE conference on
computer vision and pattern recognition. 2017: 3193-3202.
[138]Peng C, Zhang X, Yu G, et al. Large kernel matters--improve semantic
segmentation by global convolutional network[C]//Proceedings of the IEEE
conference on computer vision and pattern recognition. 2017: 4353-4361.
[139]Maaz M, Shaker A, Cholakkal H, et al. Edgenext: efficiently amalgamated cnntransformer architecture for mobile vision applications[C]//European Conference
on Computer Vision. Cham: Springer Nature Switzerland, 2022: 3-20.
[140]Yu C, Wang J, Gao C, et al. Context prior for scene segmentation[C]//Proceedings
of the IEEE/CVF conference on computer vision and pattern recognition. 2020:
12416-12425.
[141]Takahashi N, Mitsufuji Y. Densely connected multi-dilated convolutional
networks for dense prediction tasks[C]//Proceedings of the IEEE/CVF conference
on computer vision and pattern recognition. 2021: 993-1002.
[142]Ke T W, Hwang J J, Liu Z, et al. Adaptive affinity fields for semantic
segmentation[C]//Proceedings of the European conference on computer vision
(ECCV). 2018: 587-602.
[143]Yu C, Wang J, Peng C, et al. Learning a discriminative feature network for
semantic segmentation[C]//Proceedings of the IEEE conference on computer
vision and pattern recognition. 2018: 1857-1866.
[144]Seichter D, Köhler M, Lewandowski B, et al. Efficient rgb-d semantic
segmentation for indoor scene analysis[C]//2021 IEEE international conference on
robotics and automation (ICRA). IEEE, 2021: 13525-13531.
[145]Valada A, Mohan R, Burgard W. Self-supervised model adaptation for
multimodal semantic segmentation[J]. International Journal of Computer Vision,
2020, 128(5): 1239-1285.
[146]Ding H, Jiang X, Shuai B, et al. Semantic correlation promoted shape-variant
context for segmentation[C]//Proceedings of the IEEE/CVF Conference on
Computer Vision and Pattern Recognition. 2019: 8885-8894.
[147]Ding H, Jiang X, Liu A Q, et al. Boundary-aware feature propagation for scene
segmentation[C]//Proceedings of the IEEE/CVF International Conference on
Computer Vision. 2019: 6819-6829.
[148]Zhu Z, Xu M, Bai S, et al. Asymmetric non-local neural networks for semantic
segmentation[C]//Proceedings of the IEEE/CVF international conference on
computer vision. 2019: 593-602.
[149]Awan K A, Din I U, Almogren A, et al. NeuroTrust—artificial-neural-networkbased intelligent trust management mechanism for large-scale internet of medical
things[J]. IEEE Internet of Things Journal, 2020, 8(21): 15672-15682.
[150]Khan M A, Din I U, Kim B S, et al. Visualization of Remote Patient Monitoring
System Based on Internet of Medical Things[J]. Sustainability, 2023, 15(10): 8120.
[151]Jha D, Smedsrud P H, Riegler M A, et al. Kvasir-seg: A segmented polyp
dataset[C]//MultiMedia Modeling: 26th International Conference, MMM 2020,
Daejeon, South Korea, January 5–8, 2020, Proceedings, Part II 26. Springer
International Publishing, 2020: 451-462.
[152]Zhao F, Xie X. An overview of interactive medical image segmentation[J].
Annals of the BMVA, 2013, 2013(7): 1-22.
[153]Lê M, Unkelbach J, Ayache N, et al. Gpssi: Gaussian process for sampling
segmentations of images[C]//Medical Image Computing and Computer-Assisted
Intervention–MICCAI 2015: 18th International Conference, Munich, Germany,
October 5-9, 2015, Proceedings, Part III 18. Springer International Publishing,
2015: 38-46.
[154]Bai W, Sinclair M, Tarroni G, et al. Human-level CMR image analysis with deep
fully convolutional networks[J]. 2017.
[155]Chu Z, Singh S, Sowmya A. Robust Automated Tumour Segmentation Network
Using 3D Direction-Wise Convolution and Transformer[J]. Journal of Imaging
Informatics in Medicine, 2024: 1-10.
[156]Roth H R, Lu L, Lay N, et al. Spatial aggregation of holistically-nested
convolutional neural networks for automated pancreas localization and
segmentation[J]. Medical image analysis, 2018, 45: 94-107.
[157]Ketkar N, Santana E. Deep learning with Python[M]. Berkeley, CA: Apress, 2017.
pp. 97–111.
[158]Abadi M, Barham P, Chen J, et al. {TensorFlow}: a system for {Large-Scale}
machine learning[C]//12th USENIX symposium on operating systems design and
implementation (OSDI 16). 2016: 265-283.
[159]Xu G, Zhang X, He X, et al. Levit-unet: Make faster encoders with transformer
for medical image segmentation[C]//Chinese Conference on Pattern Recognition
and Computer Vision (PRCV). Singapore: Springer Nature Singapore, 2023: 42-
53.
[160]Gao G, Yu Y, Yang M, et al. Multi-scale patch based representation feature
learning for low-resolution face recognition[J]. Applied Soft Computing, 2020, 90:
106183.
[161]Jha D, Smedsrud P H, Johansen D, et al. A comprehensive study on colorectal
polyp segmentation with ResUNet++, conditional random field and test-time
augmentation[J]. IEEE journal of biomedical and health informatics, 2021, 25(6):
2029-2040.
[162]Wang Y, Yu B, Wang L, et al. 3D conditional generative adversarial networks for
high-quality PET image estimation at low dose[J]. Neuroimage, 2018, 174: 550-
[163]Fan D P, Ji G P, Zhou T, et al. Pranet: Parallel reverse attention network for polyp
segmentation[C]//International conference on medical image computing and
computer-assisted intervention. Cham: Springer International Publishing, 2020:
263-273.
[164]Jha D, Ali S, Tomar N K, et al. Real-time polyp detection, localization and
segmentation in colonoscopy using deep learning[J]. Ieee Access, 2021, 9: 40496-
40510.
[165]Zhang Y, Liu H, Hu Q. Transfuse: Fusing transformers and cnns for medical
image segmentation[C]//Medical Image Computing and Computer Assisted
Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France,
September 27–October 1, 2021, Proceedings, Part I 24. Springer International
Publishing, 2021: 14-24.
[166]Kim T, Lee H, Kim D. Uacanet: Uncertainty augmented context attention for
polyp segmentation[C]//Proceedings of the 29th ACM International Conference on
Multimedia. 2021: 2167-2175.
[167]Wang J, Huang Q, Tang F, et al. Stepwise feature fusion: Local guides
global[C]//International Conference on Medical Image Computing and ComputerAssisted Intervention. Cham: Springer Nature Switzerland, 2022: 110-120.
[168]Wang J, Wei L, Wang L, et al. Boundary-aware transformers for skin lesion
segmentation[C]//Medical Image Computing and Computer Assisted Intervention–
MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–
October 1, 2021, Proceedings, Part I 24. Springer International Publishing, 2021:
206-216.
[169]Liu C, Xie H, Zha Z, et al. Bidirectional attention-recognition model for finegrained object classification[J]. IEEE Transactions on Multimedia, 2019, 22(7):
1785-1795.
[170]Min S, Yao H, Xie H, et al. Domain-oriented semantic embedding for zero-shot
learning[J]. IEEE Transactions on Multimedia, 2020, 23: 3919-3930.
[171]Min S, Yao H, Xie H, et al. Multi-objective matrix normalization for fine-grained
visual recognition[J]. IEEE Transactions on Image Processing, 2020, 29: 4996-
[172]Tian Z, Shen C, Chen H, et al. Fcos: Fully convolutional one-stage object
detection[C]//Proceedings of the IEEE/CVF international conference on computer
vision. 2019: 9627-9636.
[173]He K, Gkioxari G, Dollár P, et al. Mask r-cnn[C]//Proceedings of the IEEE
international conference on computer vision. 2017: 2961-2969.
[174]Han Z, Jian M, Wang G G. ConvUNeXt: An efficient convolution neural network
for medical image segmentation[J]. Knowledge-Based Systems, 2022, 253:
109512.
[175]Yuan K, Guo S, Liu Z, et al. Incorporating convolution designs into visual
transformers[C]//Proceedings of the IEEE/CVF International Conference on
Computer Vision. 2021: 579-588.
[176]贾熹滨, 郭雄, 王珞, 等. 一种边界增强的医学图像小样本分割网络[J].
自动化学报, 2023, 49: 1-14.
[177]He J, Chen J N, Liu S, et al. Transfg: A transformer architecture for fine-grained
recognition[C]//Proceedings of the AAAI Conference on Artificial Intelligence.
2022, 36(1): 852-860.
[178]Wang P, Cai Z, Yang H, et al. Omni-detr: Omni-supervised object detection with
transformers[C]//Proceedings of the IEEE/CVF conference on computer vision and
pattern recognition. 2022: 9367-9376.
[179]Zhang G, Luo Z, Yu Y, et al. Accelerating DETR convergence via semanticaligned matching[C]//Proceedings of the IEEE/CVF conference on computer
vision and pattern recognition. 2022: 949-958.
[180]Li F, Zhang H, Liu S, et al. Dn-detr: Accelerate detr training by introducing query
denoising[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and
Pattern Recognition. 2022: 13619-13627.
[181]Gupta A, Narayan S, Joseph K J, et al. Ow-detr: Open-world detection
transformer[C]//Proceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition. 2022: 9235-9244.
[182]Zang Y, Li W, Zhou K, et al. Open-vocabulary detr with conditional
matching[C]//European Conference on Computer Vision. Cham: Springer Nature
Switzerland, 2022: 106-122.
[183]Strudel R, Garcia R, Laptev I, et al. Segmenter: Transformer for semantic
segmentation[C]//Proceedings of the IEEE/CVF international conference on
computer vision. 2021: 7262-7272.
[184]Zheng S, Lu J, Zhao H, et al. Rethinking semantic segmentation from a sequenceto-sequence perspective with transformers[C]//Proceedings of the IEEE/CVF
conference on computer vision and pattern recognition. 2021: 6881-6890.
[185]Yang Y, Zhang L, Ren L, et al. MMViT-Seg: A lightweight transformer and CNN
fusion network for COVID-19 segmentation[J]. Computer Methods and Programs
in Biomedicine, 2023, 230: 107348.
[186]Li X, Pang S, Zhang R, et al. ATTransUNet: An enhanced hybrid transformer
architecture for ultrasound and histopathology image segmentation[J]. Computers
in Biology and Medicine, 2023, 152: 106365.
[187]Gao C, Ye H, Cao F, et al. Multiscale fused network with additive channel–spatial
attention for image segmentation[J]. Knowledge-Based Systems, 2021, 214:
106754.
[188]Lin F, Liang Z, Wu S, et al. Structtoken: Rethinking semantic segmentation with
structural prior[J]. IEEE Transactions on Circuits and Systems for Video
Technology, 2023.
[189]Park K B, Lee J Y. SwinE-Net: Hybrid deep learning approach to novel polyp
segmentation using convolutional neural network and Swin Transformer[J].
Journal of Computational Design and Engineering, 2022, 9(2): 616-632.
[190]Liu Y, Wang H, Chen Z, et al. TransUNet+: Redesigning the skip connection to
enhance features in medical image segmentation[J]. Knowledge-Based Systems,
2022, 256: 109859.
[191]Tang P, Yang P, Nie D, et al. Unified medical image segmentation by learning
from uncertainty in an end-to-end manner[J]. Knowledge-Based Systems, 2022,
241: 108215.
[192]Qi M, Liu L, Zhuang S, et al. FTC-net: fusion of transformer and CNN features
for infrared small target detection[J]. IEEE Journal of Selected Topics in Applied
Earth Observations and Remote Sensing, 2022, 15: 8613-8623.
[193]Fu H, Xu Y, Lin S, et al. Deepvessel: Retinal vessel segmentation via deep
learning and conditional random field[C]//Medical Image Computing and
Computer-Assisted Intervention–MICCAI 2016: 19th International Conference,
Athens, Greece, October 17-21, 2016, Proceedings, Part II 19. Springer
International Publishing, 2016: 132-139.
[194]Yao C, Hu M, Li Q, et al. Transclaw u-net: claw u-net with transformers for
medical image segmentation[C]//2022 5th International Conference on
Information Communication and Signal Processing (ICICSP). IEEE, 2022: 280-
284.
[195]Xie Y, Zhang J, Shen C, et al. Cotr: Efficiently bridging cnn and transformer for
3d medical image segmentation[C]//Medical Image Computing and Computer
Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg,
France, September 27–October 1, 2021, Proceedings, Part III 24. Springer
International Publishing, 2021: 171-180.
[196]Zhou Z, Siddiquee M M R, Tajbakhsh N, et al. Unet++: Redesigning skip
connections to exploit multiscale features in image segmentation[J]. IEEE
transactions on medical imaging, 2019, 39(6): 1856-1867.
[197]Wang J, Sun K, Cheng T, et al. Deep high-resolution representation learning for
visual recognition[J]. IEEE transactions on pattern analysis and machine
intelligence, 2020, 43(10): 3349-3364.
[198]Srivastava A, Jha D, Chanda S, et al. MSRF-Net: a multi-scale residual fusion
network for biomedical image segmentation[J]. IEEE Journal of Biomedical and
Health Informatics, 2021, 26(5): 2252-2263.
[199]Xu G, Zhang X, Fang Y, et al. LeVit-UNet: Make Faster Encoders with
Transformer for Biomedical Image Segmentation[J]. SSRN, May. 2022.
[200]Xu Q, Ma Z, Na H E, et al. DCSAU-Net: A deeper and more compact splitattention U-Net for medical image segmentation[J]. Computers in Biology and
Medicine, 2023, 154: 106626.