Domain adaptation and representation transfer : 4th MICCAI Workshop, DART 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings. Domain Adaptation and Representation Transfer (Workshop) (4th : 2022 : Sin...最新文献

POPAR: Patch Order Prediction and Appearance Recovery for Self-supervised Medical Image Analysis. POPAR：用于自我监督医学图像分析的斑块阶次预测和外观恢复。

Domain adaptation and representation transfer : 4th MICCAI Workshop, DART 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings. Domain Adaptation and Representation Transfer (Workshop) (4th : 2022 : Sin... Pub Date : 2022-09-01 Epub Date: 2022-09-15 DOI: 10.1007/978-3-031-16852-9_8

Jiaxuan Pang, Fatemeh Haghighi, DongAo Ma, Nahid Ul Islam, Mohammad Reza Hosseinzadeh Taher, Michael B Gotway, Jianming Liang

{"title":"POPAR: Patch Order Prediction and Appearance Recovery for Self-supervised Medical Image Analysis.","authors":"Jiaxuan Pang, Fatemeh Haghighi, DongAo Ma, Nahid Ul Islam, Mohammad Reza Hosseinzadeh Taher, Michael B Gotway, Jianming Liang","doi":"10.1007/978-3-031-16852-9_8","DOIUrl":"10.1007/978-3-031-16852-9_8","url":null,"abstract":"Vision transformer-based self-supervised learning (SSL) approaches have recently shown substantial success in learning visual representations from unannotated photographic images. However, their acceptance in medical imaging is still lukewarm, due to the significant discrepancy between medical and photographic images. Consequently, we propose POPAR (patch order prediction and appearance recovery), a novel vision transformer-based self-supervised learning framework for chest X-ray images. POPAR leverages the benefits of vision transformers and unique properties of medical imaging, aiming to simultaneously learn patch-wise high-level contextual features by correcting shuffled patch orders and fine-grained features by recovering patch appearance. We transfer POPAR pretrained models to diverse downstream tasks. The experiment results suggest that (1) POPAR outperforms state-of-the-art (SoTA) self-supervised models with vision transformer backbone; (2) POPAR achieves significantly better performance over all three SoTA contrastive learning methods; and (3) POPAR also outperforms fully-supervised pretrained models across architectures. In addition, our ablation study suggests that to achieve better performance on medical imaging tasks, both fine-grained and global contextual features are preferred. All code and models are available at GitHub.com/JLiangLab/POPAR.","PeriodicalId":72837,"journal":{"name":"Domain adaptation and representation transfer : 4th MICCAI Workshop, DART 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings. Domain Adaptation and Representation Transfer (Workshop) (4th : 2022 : Sin...","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9728135/pdf/nihms-1846235.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10361125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Discriminative, Restorative, and Adversarial Learning: Stepwise Incremental Pretraining. 判别、恢复和对抗学习：逐步递增预训练。

Domain adaptation and representation transfer : 4th MICCAI Workshop, DART 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings. Domain Adaptation and Representation Transfer (Workshop) (4th : 2022 : Sin... Pub Date : 2022-09-01 Epub Date: 2022-09-15 DOI: 10.1007/978-3-031-16852-9_7

Zuwei Guo, Nahid Ui Islam, Michael B Gotway, Jianming Liang

{"title":"Discriminative, Restorative, and Adversarial Learning: Stepwise Incremental Pretraining.","authors":"Zuwei Guo, Nahid Ui Islam, Michael B Gotway, Jianming Liang","doi":"10.1007/978-3-031-16852-9_7","DOIUrl":"10.1007/978-3-031-16852-9_7","url":null,"abstract":"Uniting three self-supervised learning (SSL) ingredients (discriminative, restorative, and adversarial learning) enables collaborative representation learning and yields three transferable components: a discriminative encoder, a restorative decoder, and an adversary encoder. To leverage this advantage, we have redesigned five prominent SSL methods, including Rotation, Jigsaw, Rubik's Cube, Deep Clustering, and TransVW, and formulated each in a United framework for 3D medical imaging. However, such a United framework increases model complexity and pretraining difficulty. To overcome this difficulty, we develop a stepwise incremental pretraining strategy, in which a discriminative encoder is first trained via discriminative learning, the pretrained discriminative encoder is then attached to a restorative decoder, forming a skip-connected encoder-decoder, for further joint discriminative and restorative learning, and finally, the pretrained encoder-decoder is associated with an adversarial encoder for final full discriminative, restorative, and adversarial learning. Our extensive experiments demonstrate that the stepwise incremental pretraining stabilizes United models training, resulting in significant performance gains and annotation cost reduction via transfer learning for five target tasks, encompassing both classification and segmentation, across diseases, organs, datasets, and modalities. This performance is attributed to the synergy of the three SSL ingredients in our United framework unleashed via stepwise incremental pretraining. All codes and pretrained models are available at GitHub.com/JLiangLab/StepwisePretraining.","PeriodicalId":72837,"journal":{"name":"Domain adaptation and representation transfer : 4th MICCAI Workshop, DART 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings. Domain Adaptation and Representation Transfer (Workshop) (4th : 2022 : Sin...","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9728134/pdf/nihms-1846234.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10729956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Benchmarking and Boosting Transformers for Medical Image Classification. 用于医学图像分类的基准和提升变换器。

Domain adaptation and representation transfer : 4th MICCAI Workshop, DART 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings. Domain Adaptation and Representation Transfer (Workshop) (4th : 2022 : Sin... Pub Date : 2022-09-01 Epub Date: 2022-09-15 DOI: 10.1007/978-3-031-16852-9_2

DongAo Ma, Mohammad Reza Hosseinzadeh Taher, Jiaxuan Pang, Nahid Ui Islam, Fatemeh Haghighi, Michael B Gotway, Jianming Liang

{"title":"Benchmarking and Boosting Transformers for Medical Image Classification.","authors":"DongAo Ma, Mohammad Reza Hosseinzadeh Taher, Jiaxuan Pang, Nahid Ui Islam, Fatemeh Haghighi, Michael B Gotway, Jianming Liang","doi":"10.1007/978-3-031-16852-9_2","DOIUrl":"10.1007/978-3-031-16852-9_2","url":null,"abstract":"Visual transformers have recently gained popularity in the computer vision community as they began to outrank convolutional neural networks (CNNs) in one representative visual benchmark after another. However, the competition between visual transformers and CNNs in medical imaging is rarely studied, leaving many important questions unanswered. As the first step, we benchmark how well existing transformer variants that use various (supervised and self-supervised) pre-training methods perform against CNNs on a variety of medical classification tasks. Furthermore, given the data-hungry nature of transformers and the annotation-deficiency challenge of medical imaging, we present a practical approach for bridging the domain gap between photographic and medical images by utilizing unlabeled large-scale in-domain data. Our extensive empirical evaluations reveal the following insights in medical imaging: (1) good initialization is more crucial for transformer-based models than for CNNs, (2) self-supervised learning based on masked image modeling captures more generalizable representations than supervised models, and (3) assembling a larger-scale domain-specific dataset can better bridge the domain gap between photographic and medical images via self-supervised continuous pre-training. We hope this benchmark study can direct future research on applying transformers to medical imaging analysis. All codes and pre-trained models are available on our GitHub page https://github.com/JLiangLab/BenchmarkTransformers.","PeriodicalId":72837,"journal":{"name":"Domain adaptation and representation transfer : 4th MICCAI Workshop, DART 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings. Domain Adaptation and Representation Transfer (Workshop) (4th : 2022 : Sin...","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9646404/pdf/nihms-1846236.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40490559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Domain Adaptation and Representation Transfer: 4th MICCAI Workshop, DART 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings 领域适应和表示转移:第四届MICCAI研讨会，DART 2022，与MICCAI 2022一起举行，新加坡，2022年9月22日，会议记录

Domain adaptation and representation transfer : 4th MICCAI Workshop, DART 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings. Domain Adaptation and Representation Transfer (Workshop) (4th : 2022 : Sin... Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-16852-9

引用次数: 0