Medical image segmentation is one of the major challenges addressed by machine learning methods. Yet, deep learning methods profoundly depend on a large amount of annotated data, which is time-consuming and costly. Though, semi-supervised learning methods approach this problem by leveraging an abundant amount of unlabeled data along with a small amount of labeled data in the training process. Recently, MixUp regularizer has been successfully introduced to semi-supervised learning methods showing superior performance. MixUp augments the model with new data points through linear interpolation of the data at the input space. We argue that this option is limited. Instead, we propose ROAM, a RandOm lAyer Mixup, which encourages the network to be less confident for interpolated data points at randomly selected space. ROAM generates more data points that have never seen before, and hence it avoids over-fitting and enhances the generalization ability. We conduct extensive experiments to validate our method on three publicly available datasets on whole-brain image segmentation. ROAM achieves state-of-the-art (SOTA) results in fully supervised (89.5%) and semi-supervised (87.0%) settings with a relative improvement of up to 2.40% and 16.50%, respectively for the whole-brain segmentation.
Bournemouth University（伯恩茅斯大学）；University of Adelaide（阿德莱德大学）；Universitat Pompeu Fabra（庞培法布拉大学）；Catalan Institution for Research and Advanced Studies (ICREA)（加泰罗尼亚研究与高级研究机构（ICREA））
Highly imbalanced datasets are ubiquitous in medical image classification problems. In such problems, it is often the case that rare classes associated to less prevalent diseases are severely under-represented in labeled databases, typically resulting in poor performance of machine learning algorithms due to overfitting in the learning process. In this paper, we propose a novel mechanism for sampling training data based on the popular MixUp regularization technique, which we refer to as Balanced-MixUp. In short, Balanced-MixUp simultaneously performs regular (i.e., instance-based) and balanced…
Zhejiang University（浙江大学）；University of Notre Dame（圣母大学）
In clinical practice, medical image interpretation often involves multi-labeled classification, since the affected parts of a patient tend to present multiple symptoms or comorbidities. Recently, deep learning based frameworks have attained expert-level performance on medical image interpretation, which can be attributed partially to large amounts of accurate annotations. However, manually annotating massive amounts of medical images is impractical, while automatic annotation is fast but imprecise (possibly introducing corrupted labels). In this work, we propose a new regularization approach, …
SupReMix: Supervised Contrastive Learning for Medical Imaging Regression with Mixup
Yong Loo Lin School of Medicine, National University of Singapore（杨潞龄医学院，新加坡国立大学）；National University of Singapore（新加坡国立大学）；Duke University（杜克大学）；ETH Zurich（苏黎世联邦理工学院）
Simon Fraser University（西蒙弗雷泽大学）；Hinge Health（Hinge Health公司）
Modern deep learning training procedures rely on model regularization techniques such as data augmentation methods, which generate training samples that increase the diversity of data and richness of label information. A popular recent method, mixup, uses convex combinations of pairs of original samples to generate new samples. However, as we show in our experiments, mixup can produce undesirable synthetic samples, where the data is sampled off the manifold and can contain incorrect labels. We propose $ζ$-mixup, a generalization of mixup with provably and demonstrably desirable properties that…
For medical image analysis, segmentation models trained on one or several domains lack generalization ability to unseen domains due to discrepancies between different data acquisition policies. We argue that the degeneration in segmentation performance is mainly attributed to overfitting to source domains and domain shift. To this end, we present a novel generalizable medical image segmentation method. To be specific, we design our approach as a multi-task paradigm by combining the segmentation model with a self-supervision domain-specific image restoration (DSIR) module for model regularizati…
Computer aided diagnosis (CAD) has gained an increased amount of attention in the general research community over the last years as an example of a typical limited data application - with experiments on labeled 100k-200k datasets. Although these datasets are still small compared to natural image datasets like ImageNet1k, ImageNet21k and JFT, they are large for annotated medical datasets, where 1k-10k labeled samples are much more common. There is no baseline on which methods to build on in the low data regime. In this work we bridge this gap by providing an extensive study on medical image cla…
GenMix: Combining Generative and Mixture Data Augmentation for Medical Image Classification
School of Electrical Engineering（电气工程学院）；Korea Advanced Institute of Science and Technology（韩国科学技术院）；Department of Software Convergence（软件融合系）；Seoul Women’s University（首尔女子大学）
Southern University of Science and Technology（南方科技大学）；University of British Columbia（不列颠哥伦比亚大学）
Due to the high cost of manually annotating medical images, especially for large-scale datasets, anomaly detection has been explored through training models with only normal data. Lacking prior knowledge of true anomalies is the main reason for the limited application of previous anomaly detection methods, especially in the medical image analysis realm. In this work, we propose a one-shot anomaly detection framework, namely LesionPaste, that utilizes true anomalies from a single annotated sample and synthesizes artificial anomalous samples for anomaly detection. First, a lesion bank is constru…
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；University of Southern California（南加州大学）
Unsupervised domain adaptation (UDA) has been vastly explored to alleviate domain shifts between source and target domains, by applying a well-performed model in an unlabeled target domain via supervision of a labeled source domain. Recent literature, however, has indicated that the performance is still far from satisfactory in the presence of significant domain shifts. Nonetheless, delineating a few target samples is usually manageable and particularly worthwhile, due to the substantial performance gain. Inspired by this, we aim to develop semi-supervised domain adaptation (SSDA) for medical …
Prompt-driven Latent Domain Generalization for Medical Image Classification
Monash University（莫纳什大学）；Airdoc-Monash Research, Monash University（Airdoc-莫纳什研究，莫纳什大学）；Inception Institute of AI（Inception人工智能研究院）；University of Queensland Diamantina Institute, Dermatology Research Centre, The University of Queensland（昆士兰大学Diamantina研究所，皮肤病学研究中心，昆士兰大学）；Victorian Melanoma Service, Alfred Health（维多利亚黑色素瘤服务中心，阿尔弗雷德健康中心）
Distance Transform Guided Mixup for Alzheimer's Detection
Faculty of Engineering and Natural Sciences (VPALab), Sabanci University, Istanbul, Türkiye（工程与自然科学学院（VPALab），Sabanci大学，伊斯坦布尔，土耳其）；Center of Excellence in Data Analytics (VERIM), Sabanci University, Istanbul, Türkiye（数据分析卓越中心（VERIM），Sabanci大学，伊斯坦布尔，土耳其）
Saliency-guided and Patch-based Mixup for Long-tailed Skin Cancer Image Classification
Southern University of Science and Technology（南方科技大学）；University of British Columbia（不列颠哥伦比亚大学）；The University of Hong Kong（香港大学）；Jiaxing Research Institute, Southern University of Science and Technology（南方科技大学嘉兴研究院）
Image classification with deep neural networks has seen a surge of technological breakthroughs with promising applications in areas such as face recognition, medical imaging, and autonomous driving. In engineering problems, however, such as high-speed imaging of engine fuel injector sprays or body paint sprays, deep neural networks face a fundamental challenge related to the availability of adequate and diverse data. Typically, only thousands or sometimes even hundreds of samples are available for training. In addition, the transition between different spray classes is a continuum and requires…
As the COVID-19 pandemic aggravated the excessive workload of doctors globally, the demand for computer aided methods in medical imaging analysis increased even further. Such tools can result in more robust diagnostic pipelines which are less prone to human errors. In our paper, we present a deep neural network to which we refer to as Attention BCDU-Net, and apply it to the task of lung and heart segmentation from chest X-ray (CXR) images, a basic but ardous step in the diagnostic pipeline, for instance for the detection of cardiomegaly. We show that the fine-tuned model exceeds previous state…
Curating a large set of fully annotated training data can be costly, especially for the tasks of medical image segmentation. Scribble, a weaker form of annotation, is more obtainable in practice, but training segmentation models from limited supervision of scribbles is still challenging. To address the difficulties, we propose a new framework for scribble learning-based medical image segmentation, which is composed of mix augmentation and cycle consistency and thus is referred to as CycleMix. For augmentation of supervision, CycleMix adopts the mixup strategy with a dedicated design of random …
Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation
State Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家重点实验室，南京大学）；National Institute of Healthcare Data Science, Nanjing University（健康数据科学国家研究院，南京大学）；Nanjing Drum Tower Hospital, Nanjing, Jiangsu, China（南京鼓楼医院，南京，江苏，中国）
Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation
Nanjing University（南京大学）；Southeast University（东南大学）；Shandong Women’s University（山东女子学院）；State Key Laboratory for Novel Software Technology（现代软件技术国家重点实验室）；National Institute of Healthcare Data Science（国家医疗数据科学研究院）
Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation
Harvard Medical School（哈佛医学院）；Harvard University（哈佛大学）；Graduate School of Engineering, Tohoku University（东北大学工学研究生院）；School of Biomedical Engineering, Anhui Medical University（安徽医科大学生物医学工程学院）；Washington University in St. Louis（圣路易斯华盛顿大学）；The Hong Kong Polytechnic University（香港理工大学）
