FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images
Journal:
arXiv
Published Date:
Jun 9, 2025
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.
