This work proposes a novel approach beyond supervised learning for effective pathological image analysis, addressing the challenge of limited robust labeled data. Pathological diagnosis of diseases like cancer has conventionally relied on the evaluation of morphological features by physicians and pathologists. However, recent advancements in compute-aided diagnosis (CAD) systems are gaining significant attention as diagnostic support tools. Although the advancement of deep learning has improved CAD significantly, segmentation models typically require large pixel-level annotated dataset, and such labeling is expensive. Existing studies not based on supervised approaches still struggle with limited generalization, and no practical approach has emerged yet. To address this issue, we present a weakly supervised semantic segmentation (WSSS) model by combining class activation map and Segment Anything Model (SAM)-based pseudo-labeling. For effective pretraining, we adopt the SAM-a foundation model that is pretrained on large datasets and operates in zero-shot configurations using only coarse prompts. The proposed approach transfer enhanced Attention Dropout Layer's knowledge to SAM, thereby generating pseudo-labels. To demonstrate the superiority of the proposed method, experimental studies are conducted on histopathological breast cancer datasets. The proposed method outperformed other WSSS methods across three datasets, demonstrating its efficiency by achieving this with only 12GB of GPU memory during training. Our code is available at : https://github.com/QI-NemoSong/EPLC-SAM

翻译：本研究提出了一种超越监督学习的创新方法，用于有效的病理图像分析，以解决鲁棒标注数据有限的问题。癌症等疾病的病理诊断传统上依赖于医生和病理学家对形态学特征的评估。然而，作为诊断支持工具，计算机辅助诊断（CAD）系统的最新进展正受到广泛关注。尽管深度学习的进步显著改善了CAD系统，但分割模型通常需要大量像素级标注数据集，而此类标注成本高昂。现有非监督方法的研究仍受限于泛化能力不足，尚未出现实用化的解决方案。为解决这一问题，我们提出了一种弱监督语义分割（WSSS）模型，通过结合类别激活图与基于Segment Anything模型（SAM）的伪标注技术实现。为实现有效的预训练，我们采用SAM——一种基于大规模数据集预训练的基础模型，仅需粗略提示即可在零样本配置下运行。所提出的方法将增强型注意力丢弃层的知识迁移至SAM，从而生成伪标签。为验证所提方法的优越性，我们在组织病理学乳腺癌数据集上进行了实验研究。该方法在三个数据集上均优于其他WSSS方法，且训练期间仅需12GB GPU内存即实现高效性能，证明了其有效性。我们的代码公开于：https://github.com/QI-NemoSong/EPLC-SAM