Abstract
Accurate lesion segmentation in histopathology images is essential for diagnostic interpretation and quantitative analysis, yet it remains challenging due to the limited availability of costly pixel-level annotations. To address this, we propose FMaMIL, a novel two-stage framework for weakly supervised lesion segmentation based solely on image-level labels. In the first stage, a lightweight Mamba-based encoder is introduced to capture long-range dependencies across image patches under the MIL paradigm. To enhance spatial sensitivity and structural awareness, we design a learnable frequency-domain encoding module that supplements spatial-domain features with spectrum-based information. CAMs generated in this stage are used to guide segmentation training. In the second stage, we refine the initial pseudo labels via a CAM-guided soft-label supervision and a self-correction mechanism, enabling robust training even under label noise. Extensive experiments on both public and private histopathology datasets demonstrate that FMaMIL outperforms state-of-the-art weakly supervised methods without relying on pixel-level annotations, validating its effectiveness and potential for digital pathology applications.
Abstract (translated)
在病理图像中准确地分割病变区域对于诊断解释和定量分析至关重要，但由于昂贵的像素级注释资源有限，这一任务仍然具有挑战性。为了应对这个问题，我们提出了FMaMIL，这是一种基于仅使用图像级别标签的弱监督学习框架的新颖两阶段方法。 **第一阶段**：在该阶段中，引入了一个轻量级的以Mamba为基础的编码器，在多实例学习（MIL）范式下捕捉图像块间的长程依赖性。为了增强空间敏感性和结构感知能力，我们设计了一个可学习的频域编码模块，通过基于谱的信息补充了空域特征。该阶段生成的类激活图（CAMs）用于引导分割训练。 **第二阶段**：在这一阶段中，我们通过使用CAM指导下的软标签监督和自我校正机制来优化初始伪标签，这使得即使在存在标签噪声的情况下也能实现稳健的训练。 广泛的实验表明，无论是在公开数据集还是私有病理图像数据集中，FMaMIL的表现都优于现有的弱监督方法，并且无需依赖像素级注释。这些结果验证了该框架的有效性和其在未来数字病理学应用中的潜力。
URL
https://arxiv.org/abs/2506.07652
