医学图像分类模型通常使用来自多个数据源的训练数据集进行训练。虽然利用多个数据源对于实现模型泛化至关重要，但必须认识到，这些来源的多样性固有地引入了意外的混杂因素及其他挑战，这可能会影响模型的准确性和透明度。在医学图像分类中，尤其是在肌肉骨骼图像分类中，一个显著的混杂因素是青春期观察到的骨骼成熟诱导的骨生长。我们使用Covid-19胸部X射线数据集训练了一个深度学习模型，并展示了由于意外的混杂区域，该数据集如何导致虚假相关性。eXplanation Based Learning (XBL) 是一种深度学习方法，它通过利用模型解释来交互式地消除虚假相关性，从而超越了可解释性。这是通过集成交互式用户反馈（特别是特征标注）来实现的。在我们的研究中，我们采用了两种低要求的手动反馈机制来实施基于XBL的方法，以有效消除这些虚假相关性。我们的结果强调了XBL在存在混杂因素的情况下构建鲁棒模型的巨大潜力。
英文摘要：
Medical image classification models are frequently trained using training datasets derived from multiple data sources. While leveraging multiple data sources is crucial for achieving model generalization, it is important to acknowledge that the diverse nature of these sources inherently introduces unintended confounders and other challenges that can impact both model accuracy and transparency. A notable confounding factor in medical image classification, particularly in musculoskeletal image classification, is skeletal maturation-induced bone growth observed during adolescence. We train a deep learning model using a Covid-19 chest X-ray dataset and we showcase how this dataset can lead to spurious correlations due to unintended confounding regions. eXplanation Based Learning (XBL) is a deep learning approach that goes beyond interpretability by utilizing model explanations to interactively unlearn spurious correlations. This is achieved by integrating interactive user feedback, specifically feature annotations. In our study, we employed two non-demanding manual feedback mechanisms to implement an XBL-based approach for effectively eliminating these spurious correlations. Our results underscore the promising potential of XBL in constructing robust models even in the presence of confounding factors.
UiT The Arctic University of Norway（挪威北极大学）；Technical University of Berlin（柏林工业大学）
The recent trend of integrating multi-source Chest X-Ray datasets to improve automated diagnostics raises concerns that models learn to exploit source-specific correlations to improve performance by recognizing the source domain of an image rather than the medical pathology. We hypothesize that this effect is enforced by and leverages label-imbalance across the source domains, i.e, prevalence of a disease corresponding to a source. Therefore, in this work, we perform a thorough study of the effect of label-imbalance in multi-source training for the task of pneumonia detection on the widely use…
University of Michigan（密歇根大学）；Institute for Healthcare Policy & Innovation（医疗政策与创新研究所）；Department of Electrical Engineering and Computer Science（电气工程与计算机科学系）；Department of Radiology（放射科）；Department of Internal Medicine（内科）
While deep learning has shown promise in improving the automated diagnosis of disease based on chest X-rays, deep networks may exhibit undesirable behavior related to shortcuts. This paper studies the case of spurious class skew in which patients with a particular attribute are spuriously more likely to have the outcome of interest. For instance, clinical protocols might lead to a dataset in which patients with pacemakers are disproportionately likely to have congestive heart failure. This skew can lead to models that take shortcuts by heavily relying on the biased attribute. We explore this p…
Instrumental Variable Learning for Chest X-ray Classification
Tianjin University（天津大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；Tianjin Medical University（天津医科大学）；Tianjin Medical University General Hospital（天津医科大学总医院）；Tianjin Institute of Anesthesiology（天津市麻醉学研究所）
Technical University of Munich（慕尼黑工业大学）；Ludwig-Maximilians-University（路德维希-马克西米利安大学）；University Hospital Ludwig-Maximilians-University（路德维希-马克西米利安大学医院）
本研究使用chest X-ray 14数据集发现，将胸部X光片训练分辨率提升至1024 x 1024像素可获最佳分类性能，低分辨率会因无法识别细微病理而导致模型依赖虚假特征。
This study explores the use of the Dirichlet Variational Autoencoder (DirVAE) for learning disentangled latent representations of chest X-ray (CXR) images. Our working hypothesis is that distributional sparsity, as facilitated by the Dirichlet prior, will encourage disentangled feature learning for the complex task of multi-label classification of CXR images. The DirVAE is trained using CXR images from the CheXpert database, and the predictive capacity of multi-modal latent representations learned by DirVAE models is investigated through implementation of an auxiliary multi-label classificatio…
Hybrid Explanation-Guided Learning for Transformer-Based Chest X-Ray Diagnosis
ARTORG Center for Biomedical Engineering Research, University of Bern（ARTORG生物医学工程研究中心，伯恩大学）；Inselspital (Bern University Hospital)（Inselspital（伯恩大学医院））；Insel Gruppe Bern Universitätsinstitut für Diagnostische, Interventionelle und Pädiatrische Radiologie（Bern大学诊断、介入和儿科放射学研究所）；Department of Radiation Oncology, Inselspital, Bern University Hospital（放射肿瘤科，Inselspital，伯恩大学医院）
University of South Florida（南佛罗里达大学）；University of California, San Francisco（加州大学旧金山分校）
Identifying who is infected with the Covid-19 virus is critical for controlling its spread. X-ray machines are widely available worldwide and can quickly provide images that can be used for diagnosis. A number of recent studies claim it may be possible to build highly accurate models, using deep learning, to detect Covid-19 from chest X-ray images. This paper explores the robustness and generalization ability of convolutional neural network models in diagnosing Covid-19 disease from frontal-view (AP/PA), raw chest X-ray images that were lung field cropped. Some concerning observations are made…
Mask of truth: model sensitivity to unexpected regions of medical images
IT University of Copenhagen（丹麦哥本哈根技术大学）；Copenhagen University Hospital, Herlev and Gentofte（哥本哈根大学医院，赫尔勒夫和琴托夫）；Radiological AI Testcenter（放射学AI测试中心）；Department of Radiology, Zealand University Hospital（放射科， Zealand大学医院）
Benchmarking Dependence Measures to Prevent Shortcut Learning in Medical Imaging
Hertie Institute for AI in Brain Health, University of Tübingen（蒂宾根大学赫蒂脑健康人工智能研究所）；University Hospital of Tübingen（蒂宾根大学医院）；University of Stuttgart（斯图加特大学）；University of Bern（伯尔尼大学）；Inselspital, Bern University Hospital（伯尔尼大学医院 Inselspital）；Stanford University（斯坦福大学）
