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.

翻译：深度学习正在深刻变革医学影像与放射学领域，使计算机断层扫描（CT）和X光扫描等医学图像中病理特征的识别成为可能。然而，深度学习模型在分割任务中的性能常常受限于对大量标注数据集的需求。为应对这一挑战，本研究通过可解释人工智能与反事实解释生成的视角，探索了弱监督语义分割的能力。本研究旨在开发一种基于生成模型的反事实修复方法（COIN），通过将预测分类标签从异常翻转为正常来实现病理区域的分割。例如，当分类器判定输入医学图像X为异常（即存在病理特征）时，生成模型致力于修复异常区域，从而逆转分类器的原始预测标签。该方法无需依赖预分割掩膜即可生成精确的病理分割结果。关键在于，我们仅使用图像级标签——这类标签的获取难度远低于制作精细分割掩膜。通过在爱沙尼亚塔尔图大学医院采集的CT图像上对合成目标和真实肾脏肿瘤进行分割实验，验证了该方法的效果。结果表明，COIN方法显著优于RISE、ScoreCAM、LayerCAM等既定归因方法，以及Singla等人提出的另一种反事实解释方法。该证据表明，COIN是CT图像肿瘤语义分割的有效方法，并在标注数据稀缺的医疗领域推动了深度学习应用的便捷化与高效化进程。