Shape-intensity knowledge distillation for robust medical image segmentation

Abstract

Many medical image segmentation methods have achieved impressive results. Yet, most existing methods do not take into account the shape-intensity prior information. This may lead to implausible segmentation results, in particular for images of unseen datasets. In this paper, we propose a novel approach to incorporate joint shape-intensity prior information into the segmentation network. Specifically, we first train a segmentation network (regarded as the teacher network) on class-wise averaged training images to extract valuable shape-intensity information, which is then transferred to a student segmentation network with the same network architecture as the teacher via knowledge distillation. In this way, the student network regarded as the final segmentation model can effectively integrate the shape-intensity prior information, yielding more accurate segmentation results. Despite its simplicity, experiments on five medical image segmentation tasks of different modalities demonstrate that the proposed Shape-Intensity Knowledge Distillation (SIKD) consistently improves several baseline models (including recent MaxStyle and SAMed) under intra-dataset evaluation, and significantly improves the cross-dataset generalization ability. The source code will be publicly available after acceptance.

Graphical abstract

Keywords

medical image segmentation / knowledge distillation / shape-intensity prior / deep neural network

References

[1] Wang H, Dong L, Sun M . Local feature aggregation algorithm based on graph convolutional network. Frontiers of Computer Science, 2022, 16( 3): 163309

[2] Wang T, Li J, Wu H N, Li C, Snoussi H, Wu Y . ResLNet: deep residual LSTM network with longer input for action recognition. Frontiers of Computer Science, 2022, 16( 6): 166334

[3] Xu H, Chen Z, Zhang Y, Geng X, Mi S, Yang Z . Weakly supervised temporal action localization with proxy metric modeling. Frontiers of Computer Science, 2023, 17( 2): 172309

[4] Zhang Y, Wang Z, Zhou J, Mi S . Person video alignment with human pose registration. Frontiers of Computer Science, 2023, 17( 4): 174324

[5] Tan S, Zhang L, Shu X, Wang Z . A feature-wise attention module based on the difference with surrounding features for convolutional neural networks. Frontiers of Computer Science, 2023, 17( 6): 176338

[6] Guo M, Sheng H, Zhang Z, Huang Y, Chen X, Wang C, Zhang J . CW-YOLO: joint learning for mask wearing detection in low-light conditions. Frontiers of Computer Science, 2023, 17( 6): 176710

[7] Wu Z, Gan Y, Xu T, Wang F . Graph-Segmenter: graph transformer with boundary-aware attention for semantic segmentation. Frontiers of Computer Science, 2024, 18( 5): 185327

[8] Ruan H, Song H, Liu B, Cheng Y, Liu Q . Intellectual property protection for deep semantic segmentation models. Frontiers of Computer Science, 2023, 17( 1): 171306

[9] Ji Z, Ni J, Liu X, Pang Y . Teachers cooperation: team-knowledge distillation for multiple cross-domain few-shot learning. Frontiers of Computer Science, 2023, 17( 2): 172312

[10] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In: Proceedings of the 18th International Conference on Medical Image Computing and Computer Assisted Intervention. 2015, 234−241

[11] Çiçek O, Abdulkadir A, Lienkamp S S, Brox T, Ronneberger O. 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Proceedings of the 19th International Conference on Medical Image Computing and Computer Assisted Intervention. 2016, 424−432

[12] Zhou Z, Siddiquee M M R, Tajbakhsh N, Liang J . UNet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Transactions on Medical Imaging, 2020, 39( 6): 1856–1867

[13] Oktay O, Schlemper J, Le Folgoc L, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla N Y, Kainz B, Glocker B, Rueckert D. Attention U-Net: learning where to look for the pancreas. In: Proceedings of the 1st Conference on Medical Imaging with Deep Learning. 2018

[14] Isensee F, Jaeger P F, Kohl S A A, Petersen J, Maier-Hein K H . nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 2021, 18( 2): 203–211

[15] Shi T, Boutry N, Xu Y, Géraud T . Local intensity order transformation for robust curvilinear object segmentation. IEEE Transactions on Image Processing, 2022, 31: 2557–2569

[16] Billot B, Greve D N, Puonti O, Thielscher A, Van Leemput K, Fischl B, Dalca A V, Iglesias J E, ADNI . SynthSeg: segmentation of brain MRI scans of any contrast and resolution without retraining. Medical Image Analysis, 2023, 86: 102789

[17] Gut D, Tabor Z, Szymkowski M, Rozynek M, Kucybała I, Wojciechowski W. Benchmarking of deep architectures for segmentation of medical images. IEEE Transactions on Medical Imaging, 41(11): 3231−3241

[18] Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N. An image is worth 16x16 words: transformers for image recognition at scale. In: Proceedings of the 9th International Conference on Learning Representations. 2021

[19] Zheng S, Lu J, Zhao H, Zhu X, Luo Z, Wang Y, Fu Y, Feng J, Xiang T, Torr P H S, Zhang L. Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021, 6877−6886

[20] Xie E, Wang W, Yu Z, Anandkumar A, Alvarez J M, Luo P. SegFormer: simple and efficient design for semantic segmentation with transformers. In: Proceedings of the 35th International Conference on Neural Information Processing Systems. 2021, 924

[21] Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Lu L, Yuille A L, Zhou Y. TransUNet: transformers make strong encoders for medical image segmentation. 2021, arXiv preprint arXiv: 2102.04306

[22] Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B. Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021, 9992−10002

[23] Cao H, Wang Y, Chen J, Jiang D, Zhang X, Tian Q, Wang M. Swin-Unet: Unet-like pure transformer for medical image segmentation. In: Proceedings of the European Conference on Computer Vision. 2023, 205−218

[24] Sun J, Darbehani F, Zaidi M, Wang B. SAUNet: shape attentive U-Net for interpretable medical image segmentation. In: Proceedings of the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention. 2020, 797−806

[25] Yan Z, Yang X, Cheng K T . Enabling a single deep learning model for accurate gland instance segmentation: a shape-aware adversarial learning framework. IEEE Transactions on Medical Imaging, 2020, 39( 6): 2176–2189

[26] Li L, Zimmer V A, Schnabel J A, Zhuang X . AtrialJSQnet: a new framework for joint segmentation and quantification of left atrium and scars incorporating spatial and shape information. Medical Image Analysis, 2022, 76: 102303

[27] Ning Z, Zhong S, Feng Q, Chen W, Zhang Y . SMU-Net: saliency-guided morphology-aware U-Net for breast lesion segmentation in ultrasound image. IEEE Transactions on Medical Imaging, 2022, 41( 2): 476–490

[28] Oktay O, Ferrante E, Kamnitsas K, Heinrich M, Bai W, Caballero J, Cook S A, de Marvao A, Dawes T, O‘Regan D P, Kainz B, Glocker B, Rueckert D . Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation. IEEE Transactions on Medical Imaging, 2018, 37( 2): 384–395

[29] Ravishankar H, Venkataramani R, Thiruvenkadam S, Sudhakar P, Vaidya V. Learning and incorporating shape models for semantic segmentation. In: Proceedings of the 20th International Conference on Medical Image Computing and Computer Assisted Intervention. 2017, 203−211

[30] Larrazabal A J, Martínez C, Glocker B, Ferrante E . Post-DAE: anatomically plausible segmentation via post-processing with denoising autoencoders. IEEE Transactions on Medical Imaging, 2020, 39( 12): 3813–3820

[31] Chen C, Hammernik K, Ouyang C, Qin C, Bai W, Rueckert D. Cooperative training and latent space data augmentation for robust medical image segmentation. In: Proceedings of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention. 2021, 149−159

[32] Painchaud N, Skandarani Y, Judge T, Bernard O, Lalande A, Jodoin P M . Cardiac segmentation with strong anatomical guarantees. IEEE Transactions on Medical Imaging, 2020, 39( 11): 3703–3713

[33] Girum K B, Crehange G, Lalande A . Learning with context feedback loop for robust medical image segmentation. IEEE Transactions on Medical Imaging, 2021, 40( 6): 1542–1554

[34] Zotti C, Luo Z, Lalande A, Jodoin P M . Convolutional neural network with shape prior applied to cardiac MRI segmentation. IEEE Journal of Biomedical and Health Informatics, 2019, 23( 3): 1119–1128

[35] Tilborghs S, Bogaert J, Maes F . Shape constrained CNN for segmentation guided prediction of myocardial shape and pose parameters in cardiac MRI. Medical Image Analysis, 2022, 81: 102533

[36] Mirikharaji Z, Hamarneh G. Star shape prior in fully convolutional networks for skin lesion segmentation. In: Proceedings of the 21st International Conference on Medical Image Computing and Computer Assisted Intervention. 2018, 737−745

[37] Guo F, Ng M, Kuling G, Wright G . Cardiac MRI segmentation with sparse annotations: ensembling deep learning uncertainty and shape priors. Medical Image Analysis, 2022, 81: 102532

[38] Wei H, Ma J, Zhou Y, Xue W, Ni D . Co-learning of appearance and shape for precise ejection fraction estimation from echocardiographic sequences. Medical Image Analysis, 2023, 84: 102686

[39] Yang J, Duncan J S . 3D image segmentation of deformable objects with joint shape-intensity prior models using level sets. Medical Image Analysis, 2004, 8( 3): 285–294

[40] Wang J, Cheng Y, Guo C, Wang Y, Tamura S . Shape-intensity prior level set combining probabilistic atlas and probability map constrains for automatic liver segmentation from abdominal CT images. International Journal of Computer Assisted Radiology and Surgery, 2016, 11( 5): 817–826

[41] Hinton G, Vinyals O, Dean J. Distilling the knowledge in a neural network. In: Proceedings of the NeurIPS 2014 Deep Learning Workshop. 2014

[42] Xiang T, Zhang C, Liu D, Song Y, Huang H, Cai W. BiO-Net: learning recurrent Bi-directional connections for encoder-decoder architecture. In: Proceedings of the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention. 2020, 74−84

[43] Feng S, Zhao H, Shi F, Cheng X, Wang M, Ma Y, Xiang D, Zhu W, Chen X . CPFNet: context pyramid fusion network for medical image segmentation. IEEE Transactions on Medical Imaging, 2020, 39( 10): 3008–3018

[44] Tajbakhsh N, Jeyaseelan L, Li Q, Chiang J N, Wu Z, Ding X . Embracing imperfect datasets: a review of deep learning solutions for medical image segmentation. Medical Image Analysis, 2020, 63: 101693

[45] Xie X, Niu J, Liu X, Chen Z, Tang S, Yu S . A survey on incorporating domain knowledge into deep learning for medical image analysis. Medical Image Analysis, 2021, 69: 101985

[46] Wang L, Yoon K J . Knowledge distillation and student-teacher learning for visual intelligence: a review and new outlooks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44( 6): 3048–3068

[47] Gou J, Yu B, Maybank S J, Tao D . Knowledge distillation: a survey. International Journal of Computer Vision, 2021, 129( 6): 1789–1819

[48] Yi X, Walia E, Babyn P . Generative adversarial network in medical imaging: a review. Medical Image Analysis, 2019, 58: 101552

[49] Jafari M, Francis S, Garibaldi J M, Chen X . LMISA: a lightweight multi-modality image segmentation network via domain adaptation using gradient magnitude and shape constraint. Medical Image Analysis, 2022, 81: 102536

[50] Ba J, Caruana R. Do deep nets really need to be deep? In: Proceedings of the 27th International Conference on Neural Information Processing Systems. 2014, 2654−2662

[51] Tian Y, Krishnan D, Isola P. Contrastive representation distillation. In: Proceedings of the 8th International Conference on Learning Representations. 2020

[52] Wang G H, Ge Y, Wu J . Distilling knowledge by mimicking features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44( 11): 8183–8195

[53] Ye H J, Lu S, Zhan D C . Generalized knowledge distillation via relationship matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45( 2): 1817–1834

[54] Zagoruyko S, Komodakis N. Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. In: Proc. of International Conference on Learning Representations. 2017

[55] Ge S, Liu B, Wang P, Li Y, Zeng D . Learning privacy-preserving student networks via discriminative-generative distillation. IEEE Transactions on Image Processing, 2023, 32: 116–127

[56] Liu Y, Shu C, Wang J, Shen C . Structured knowledge distillation for dense prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45( 6): 7035–7049

[57] Wang Y, Zhou W, Jiang T, Bai X, Xu Y. Intra-class feature variation distillation for semantic segmentation. In: Proceedings of the 16th European Conference on Computer Vision. 2020, 346−362

[58] Qin D, Bu J J, Liu Z, Shen X, Zhou S, Gu J J, Wang Z H, Wu L, Dai H F . Efficient medical image segmentation based on knowledge distillation. IEEE Transactions on Medical Imaging, 2021, 40( 12): 3820–3831

[59] Shu C, Liu Y, Gao J, Yan Z, Shen C. Channel-wise knowledge distillation for dense prediction. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021, 5291−5300

[60] Tian Z, Chen P, Lai X, Jiang L, Liu S, Zhao H, Yu B, Yang M C, Jia J . Adaptive perspective distillation for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45( 2): 1372–1387

[61] Yang C, Zhou H, An Z, Jiang X, Xu Y, Zhang Q. Cross-image relational knowledge distillation for semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022, 12309−12318

[62] Gupta S, Hoffman J, Malik J. Cross modal distillation for supervision transfer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016, 2827−2836

[63] Hu M, Maillard M, Zhang Y, Ciceri T, La Barbera G, Bloch I, Gori P. Knowledge distillation from multi-modal to mono-modal segmentation networks. In: Proceedings of the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention. 2020, 772−781

[64] Dou Q, Liu Q, Heng P A, Glocker B . Unpaired multi-modal segmentation via knowledge distillation. IEEE Transactions on Medical Imaging, 2020, 39( 7): 2415–2425

[65] Li K, Yu L, Wang S, Heng P A. Towards cross-modality medical image segmentation with online mutual knowledge distillation. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence. 2020, 775−783

[66] Hou Y, Ma Z, Liu C, Loy C C. Learning lightweight lane detection CNNs by self attention distillation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019, 1013−1021

[67] Zhang L, Bao C, Ma K . Self-distillation: towards efficient and compact neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44( 8): 4388–4403

[68] Bernard O, Lalande A, Zotti C, Cervenansky F, Yang X, Heng P A, Cetin I, Lekadir K, Camara O, Gonzalez Ballester M A, Sanroma G, Napel S, Petersen S, Tziritas G, Grinias E, Khened M, Kollerathu V A, Krishnamurthi G, Rohe M M, Pennec X, Sermesant M, Isensee F, Jäger P, Maier-Hein K H, Full P M, Wolf I, Engelhardt S, Baumgartner C F, Koch L M, Wolterink J M, Išgum I, Jang Y, Hong Y, Patravali J, Jain S, Humbert O, Jodoin P M. Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Transactions on Medical Imaging, 2018, 37(11): 2514−2525

[69] Campello V M, Gkontra P, Izquierdo C, Martín-Isla C, Sojoudi A, Full P M, Maier-Hein K, Zhang Y, He Z, Ma J, Parreno M, Albiol A, Kong F, Shadden S C, Acero J C, Sundaresan V, Saber M, Elattar M, Li H, Menze B, Khader F, Haarburger C, Scannell C M, Veta M, Carscadden A, Punithakumar K, Liu X, Tsaftaris S A, Huang X, Yang X, Li L, Zhuang X, Vilades D, Descalzo M L, Guala A, Mura L L, Friedrich M G, Garg R, Lebel J, Henriques F, Karakas M, Çavuş E, Petersen S E, Escalera S, Seguí S, Rodríguez-Palomares J F, Lekadir K . Multi-Centre, multi-vendor and multi-disease cardiac segmentation: the M&Ms challenge. IEEE Transactions on Medical Imaging, 2021, 40( 12): 3543–3554

[70] Landman B, Xu Z, Igelsias J, Styner M, Langerak T, Klein A. MICCAI multi-atlas labeling beyond the cranial vault-workshop and challenge. In: Proceedings of the MICCAI Multi-Atlas Labeling Beyond Cranial Vault-Workshop Challenge. 2015

[71] Ma J, Zhang Y, Gu S, Zhu C, Ge C, Zhang Y, An X, Wang C, Wang Q, Liu X, Cao S, Zhang Q, Liu S, Wang Y, Li Y, He J, Yang X. AbdomenCT-1K: is abdominal organ segmentation a solved problem? IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(10): 6695−6714

[72] Fan D P, Ji G P, Zhou T, Chen G, Fu H, Shen J, Shao L. PraNet: parallel reverse attention network for polyp segmentation. In: Proceedings of the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention. 2020, 263−273

[73] Jha D, Smedsrud P H, Riegler M A, Halvorsen P, de Lange T, Johansen D, Johansen H D. Kvasir-SEG: a segmented polyp dataset. In: Proceedings of the 26th International Conference on Multimedia Modeling. 2020, 451−462

[74] Bernal J, Sánchez F J, Fernández-Esparrach G, Gil D, Rodríguez C, Vilariño F . WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Computerized Medical Imaging and Graphics, 2015, 43: 99–111

[75] Tajbakhsh N, Gurudu S R, Liang J . Automated polyp detection in colonoscopy videos using shape and context information. IEEE Transactions on Medical Imaging, 2016, 35( 2): 630–644

[76] Silva J, Histace A, Romain O, Dray X, Granado B . Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer. International Journal of Computer Assisted Radiology and Surgery, 2014, 9( 2): 283–293

[77] Vázquez D, Bernal J, Sánchez F J, Fernández-Esparrach G, López A M, Romero A, Drozdzal M, Courville A . A benchmark for endoluminal scene segmentation of colonoscopy images. Journal of Healthcare Engineering, 2017, 2017: 4037190

[78] Orlando J I, Fu H, Barbosa Breda J, van Keer K, Bathula D R, Diaz-Pinto A, Fang R, Heng P A, Kim J, Lee J, Lee J, Li X, Liu P, Lu S, Murugesan B, Naranjo V, Phaye S S R, Shankaranarayana S M, Sikka A, Son J, van den Hengel A, Wang S, Wu J, Wu Z, Xu G, Xu Y, Yin P, Li F, Zhang X, Xu Y, Bogunović H . REFUGE challenge: a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs. Medical Image Analysis, 2020, 59: 101570

[79] Sivaswamy J, Krishnadas S R, Chakravarty A, Joshi G D, Ujjwal , Syed T A . A comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis. JSM Biomedical Imaging Data Papers, 2015, 2( 1): 1004

[80] Fumero F, Alayon S, Sanchez J L, Sigut J, Gonzalez-Hernandez M. RIM-ONE: an open retinal image database for optic nerve evaluation. In: Proceedings of the 24th International Symposium on Computer-Based Medical Systems. 2011, 1−6

[81] Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A . Dataset of breast ultrasound images. Data in Brief, 2020, 28: 104863

[82] Zhuang Z, Li N, Joseph Raj A N, Mahesh V G V, Qiu S . An RDAU-NET model for lesion segmentation in breast ultrasound images. PLoS One, 2019, 14( 8): e0221535

[83] Yap M H, Pons G, Martí J, Ganau S, Sentís M, Zwiggelaar R, Davison A K, Martí R . Automated breast ultrasound lesions detection using convolutional neural networks. IEEE Journal of Biomedical and Health Informatics, 2018, 22( 4): 1218–1226

[84] Wei J, Hu Y, Zhang R, Li Z, Zhou S K, Cui S. Shallow attention network for polyp segmentation. In: Proceedings of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention. 2021, 699−708

[85] Chen C, Li Z, Ouyang C, Sinclair M, Bai W, Rueckert D. MaxStyle: adversarial style composition for robust medical image segmentation. In: Proceedings of the 25th International Conference on Medical Image Computing and Computer Assisted Intervention. 2022, 151−161

[86] Lu Z, She C, Wang W, Huang Q . LM-Net: a light-weight and multi-scale network for medical image segmentation. Computers in Biology and Medicine, 2024, 168: 107717

[87] Azad R, Niggemeier L, Hüttemann M, Kazerouni A, Aghdam E K, Velichko Y, Bagci U, Merhof D. Beyond self-attention: deformable large kernel attention for medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2024, 1276−1286

[88] Zhang K, Liu D. Customized segment anything model for medical image segmentation. 2023, arXiv preprint arXiv: 2304.13785

[89] Kirillov A, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L, Xiao T, Whitehead S, Berg A C, Lo W Y, Dollár P, Girshick R. Segment anything. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). 2023, 3992−4003

[90] Xue Y, Tang H, Qiao Z, Gong G, Yin Y, Qian Z, Huang C, Fan W, Huang X. Shape-aware organ segmentation by predicting signed distance maps. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence. 2020, 12565−12572

[91] Fang Y, Chen C, Yuan Y, Tong K Y. Selective feature aggregation network with area-boundary constraints for polyp segmentation. In: Proceedings of the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention. 2019, 302−310

[92] Zhang R, Lai P, Wan X, Fan D J, Gao F, Wu X J, Li G. Lesion-aware dynamic kernel for polyp segmentation. In: Proceedings of the 25th International Conference on Medical Image Computing and Computer Assisted Intervention. 2022, 99−109

[93] Geirhos R, Rubisch P, Michaelis C, Bethge M, Wichmann F A, Brendel W. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. In: Proceedings of the 7th International Conference on Learning Representations. 2019

[94] Li Y, Yu Q, Tan M, Mei J, Tang P, Shen W, Yuille A L, Xie C. Shape-texture debiased neural network training. In: Proceedings of the 9th International Conference on Learning Representations. 2021

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