mlsanityData to 7 Oct 2026

XtraLight-MedMamba for Classification of Neoplastic Tubular Adenomas

arXiv 2602.04819 ↗First posted 4 Feb 2026cs.CV cs.LGcited by 0

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

Abstract (arXiv)

Accurate risk stratification of precancerous polyps during routine colonoscopy screening is a key strategy to reduce the incidence of colorectal cancer (CRC). However, assessment of low-grade dysplasia remains limited by subjective histopathologic interpretation. Advances in computational pathology and deep learning offer new opportunities to identify subtle, fine morphologic patterns associated with malignant progression that may be imperceptible to the human eye. In this work, we propose XtraLight-MedMamba, an ultra-lightweight state-space-based deep learning framework to classify neoplastic tubular adenomas from whole-slide images (WSIs). The architecture is a blend of a ConvNeXt-based shallow feature extractor with parallel vision mamba blocks to efficiently model local texture cues within global contextual structure. An integration of the Spatial and Channel Attention Bridge (SCAB) module enhances multiscale feature extraction, while the Fixed Non-Negative Orthogonal Classifier (FNOClassifier) enables substantial parameter reduction and improved generalization. The model was evaluated on a curated dataset acquired from patients with low-grade tubular adenomas, stratified into case and control cohorts based on subsequent CRC development. XtraLight-MedMamba achieved an accuracy of 97.18% and an F1-score of 0.9767 using approximately 32,000 parameters, outperforming transformer-based and conventional Mamba architectures, which have significantly higher model complexity and computational burden, making it suitable for resource-constrained areas.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Mamba for medical image analysis, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 37 of its 96 references that are in the map. First posted on arXiv 4 Feb 2026.

Builds on

37 of 96 references.

Closest ancestors · 5

Same direction, newest first

  1. 2025SlideMamba: Entropy-Based Adaptive Fusion of GNN and Mamba for Enhanced Representation Learning in Digital Pathology25 Sep
  2. 2025UltraLight Med-Vision Mamba for Classification of Neoplastic Progression in Tubular Adenomas12 Aug
  3. 2024Combining Graph Neural Network and Mamba to Capture Local and Global Tissue Spatial Relationships in Whole Slide Images5 Jun
  4. 2024Vim4Path: Self-Supervised Vision Mamba for Histopathology Images20 Apr
  5. 2024UltraLight VM-UNet: Parallel Vision Mamba Significantly Reduces Parameters for Skin Lesion Segmentation29 Mar

Other references · 32

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  5. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  6. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  7. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  8. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
  9. 2022A ConvNet for the 2020scited by 3,545
  10. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,424
  11. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
  12. 2016On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minimacited by 1,337
20 more
  1. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,182
  2. 2021EfficientNetV2: Smaller Models and Faster Trainingcited by 1,127
  3. 2013Exact solutions to the nonlinear dynamics of learning in deep linear neural networkscited by 748
  4. 2020Prevalence of Neural Collapse during the terminal phase of deep learning trainingcited by 713
  5. 2023ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoderscited by 703
  6. 2017The Implicit Bias of Gradient Descent on Separable Datacited by 649
  7. 2015An Introduction to Convolutional Neural Networkscited by 510
  8. 2017The Marginal Value of Adaptive Gradient Methods in Machine Learningcited by 248
  9. 2021A Geometric Analysis of Neural Collapse with Unconstrained Featurescited by 226
  10. 2022On The Computational Complexity of Self-Attentioncited by 137
  11. 2022MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentationcited by 59
  12. 2023Generalized Neural Collapse for a Large Number of Classescited by 39
  13. 2021UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia gradingcited by 33
  14. 2020A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networkscited by 25
  15. 2024Mamba in Vision: A Comprehensive Survey of Techniques and Applicationscited by 18
  16. 2021An Orthogonal Classifier for Improving the Adversarial Robustness of Neural Networkscited by 7
  17. 2022Predicting microsatellite instability and key biomarkers in colorectal cancer from H&E-stained images: Achieving SOTA predictive performance with fewer data using Swin Transformercited by 6
  18. 2020Quantum One-class Classification With a Distance-based Classifiercited by 2
  19. 2024Classification of Gleason Grading in Prostate Cancer Histopathology Images Using Deep Learning Techniques: YOLO, Vision Transformers, and Vision Mambacited by 2
  20. 2021Dysplasia grading of colorectal polyps through CNN analysis of WSIcited by 1

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