COIN：用于医学图像弱监督语义分割的反事实图像修复
COIN: Counterfactual inpainting for weakly supervised semantic segmentation for medical images
AI总结：
提出反事实图像修复方法COIN，通过生成模型将异常分类标签翻转为正常，利用易获取的图像级标签在无需分割掩码的情况下实现医学图像病理的精确弱监督语义分割。
摘要：
深度学习正在极大地改变医学成像和放射学领域，能够识别医学图像中的病理，包括计算机断层扫描（CT）和X射线扫描。然而，深度学习模型的性能，特别是在分割任务中，往往受到对大量标注数据集需求的限制。为了应对这一挑战，通过可解释AI和生成反事实解释的视角，探讨了弱监督语义分割的能力。本研究的范围是开发一种新颖的反事实图像修复方法（COIN），该方法通过使用生成模型将预测的分类标签从异常翻转为正常。例如，如果分类器将输入的医学图像X判定为异常，表明存在病理，生成模型旨在对异常区域进行图像修复，从而反转分类器的原始预测标签。该方法使我们能够在不依赖预先存在的分割掩码的情况下，为病理生成精确的分割。至关重要的是，该方法利用了图像级标签，这比创建详细的分割掩码要容易获取得多。通过对合成目标以及从爱沙尼亚塔尔图大学医院获取的CT图像中的实际肾脏肿瘤进行分割，证明了该方法的有效性。研究结果表明，COIN大大超越了既有的归因方法，如RISE、ScoreCAM和LayerCAM，以及Singla等人引入的一种替代反事实解释方法。这些证据表明，COIN是一种用于CT图像肿瘤语义分割的有前途的方法，并推动了深度学习应用在标注数据稀缺的医疗保健领域变得更加普及和有效。
英文摘要：
Deep learning is dramatically transforming the field of medical imaging and radiology, enabling the identification of pathologies in medical images, including computed tomography (CT) and X-ray scans. However, the performance of deep learning models, particularly in segmentation tasks, is often limited by the need for extensive annotated datasets. To address this challenge, the capabilities of weakly supervised semantic segmentation are explored through the lens of Explainable AI and the generation of counterfactual explanations. The scope of this research is development of a novel counterfactual inpainting approach (COIN) that flips the predicted classification label from abnormal to normal by using a generative model. For instance, if the classifier deems an input medical image X as abnormal, indicating the presence of a pathology, the generative model aims to inpaint the abnormal region, thus reversing the classifier's original prediction label. The approach enables us to produce precise segmentations for pathologies without depending on pre-existing segmentation masks. Crucially, image-level labels are utilized, which are substantially easier to acquire than creating detailed segmentation masks. The effectiveness of the method is demonstrated by segmenting synthetic targets and actual kidney tumors from CT images acquired from Tartu University Hospital in Estonia. The findings indicate that COIN greatly surpasses established attribution methods, such as RISE, ScoreCAM, and LayerCAM, as well as an alternative counterfactual explanation method introduced by Singla et al. This evidence suggests that COIN is a promising approach for semantic segmentation of tumors in CT images, and presents a step forward in making deep learning applications more accessible and effective in healthcare, where annotated data is scarce.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
