mlsanityData to 14 Oct 2026

FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images

arXiv 2506.07652 ↗First posted 9 Jun 2025cs.CV cs.AIcited by 1

On the map: State-space sequence models (S4, Mamba) → Mamba for medical image analysis

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Mamba for medical image analysis, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 29 of its 62 references that are in the map. First posted on arXiv 9 Jun 2025.

Builds on

29 of 62 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024MambaMIL: Enhancing Long Sequence Modeling with Sequence Reordering in Computational Pathology11 Mar

Other references · 28

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,223
  5. 2015Learning Deep Features for Discriminative Localizationcited by 2,436
  6. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,186
  7. 2018Attention-based Deep Multiple Instance Learningcited by 1,095
  8. 2024Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Modelcited by 1,056
  9. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 745
  10. 2020HiPPO: Recurrent Memory with Optimal Polynomial Projectionscited by 605
  11. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 555
  12. 2024VideoMamba: State Space Model for Efficient Video Understandingcited by 328
16 more
  1. 2015What's the Point: Semantic Segmentation with Point Supervisioncited by 298
  2. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 287
  3. 2015BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentationcited by 238
  4. 2020Learning in the Frequency Domaincited by 198
  5. 2020Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networkscited by 178
  6. 2020Single-Stage Semantic Segmentation from Image Labelscited by 167
  7. 2015Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classificationcited by 159
  8. 2016Revisiting Multiple Instance Neural Networkscited by 119
  9. 2020Annotation-efficient deep learning for automatic medical image segmentationcited by 79
  10. 2021Leveraging Auxiliary Tasks with Affinity Learning for Weakly Supervised Semantic Segmentationcited by 77
  11. 2024State Space Model for New-Generation Network Alternative to Transformers: A Surveycited by 59
  12. 2020Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentationcited by 52
  13. 2024State-space models are accurate and efficient neural operators for dynamical systemscited by 28
  14. 2018Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Imagescited by 11
  15. 2013Multiple Instance Learning with Bag Dissimilaritiescited by 5
  16. 2024Mamba2MIL: State Space Duality Based Multiple Instance Learning for Computational Pathologycited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Component-Adaptive and Lesion-Level Supervision for Improved Small Structure Segmentation in Brain MRIcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation
  2. 2024S3-Mamba: Small-Size-Sensitive Mamba for Lesion Segmentation
  3. 2024Enhancing Weakly-Supervised Histopathology Image Segmentation with Knowledge Distillation on MIL-Based Pseudo-Labels
  4. 2024Optimizing Universal Lesion Segmentation: State Space Model-Guided Hierarchical Networks with Feature Importance Adjustment
  5. 2025MambaCAFU: Hybrid Multi-Scale and Multi-Attention Model with Mamba-Based Fusion for Medical Image Segmentation
  6. 2024MambaEviScrib: Mamba and Evidence-Guided Consistency Enhance CNN Robustness for Scribble-Based Weakly Supervised Ultrasound Image Segmentation
  7. 2024SkinMamba: A Precision Skin Lesion Segmentation Architecture with Cross-Scale Global State Modeling and Frequency Boundary Guidance
  8. 2024Weak-Mamba-UNet: Visual Mamba Makes CNN and ViT Work Better for Scribble-based Medical Image Segmentation