mlsanityData to 13 Oct 2026

UltraLight Med-Vision Mamba for Classification of Neoplastic Progression in Tubular Adenomas

arXiv 2508.09339 ↗First posted 12 Aug 2025cs.CVcited by 2

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

Abstract (arXiv)

Identification of precancerous polyps during routine colonoscopy screenings is vital for their excision, lowering the risk of developing colorectal cancer. Advanced deep learning algorithms enable precise adenoma classification and stratification, improving risk assessment accuracy and enabling personalized surveillance protocols that optimize patient outcomes. Ultralight Med-Vision Mamba, a state-space based model (SSM), has excelled in modeling long- and short-range dependencies and image generalization, critical factors for analyzing whole slide images. Furthermore, Ultralight Med-Vision Mamba's efficient architecture offers advantages in both computational speed and scalability, making it a promising tool for real-time clinical deployment.

Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Placed in Mamba for medical image analysis, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 7 of its 14 references that are in the map. First posted on arXiv 12 Aug 2025.

Builds on

7 of 14 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024UltraLight VM-UNet: Parallel Vision Mamba Significantly Reduces Parameters for Skin Lesion Segmentation29 Mar

Other references · 6

  1. 2017Attention Is All You Needcited by 60,106
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,043
  3. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,111
  4. 2024Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Modelcited by 1,055
  5. 2022MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentationcited by 59
  6. 2017Deep-Learning for Classification of Colorectal Polyps on Whole-Slide Imagescited by 26

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026XtraLight-MedMamba for Classification of Neoplastic Tubular Adenomascited by 0
  2. 2026Decoding Future Risk: Deep Learning Analysis of Tubular Adenoma Whole-Slide Imagescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024MedMamba: Vision Mamba for Medical Image Classification
  2. 2024LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation
  3. 2024SAM-Mamba: Mamba Guided SAM Architecture for Generalized Zero-Shot Polyp Segmentation
  4. 2024A Comprehensive Survey of Mamba Architectures for Medical Image Analysis: Classification, Segmentation, Restoration and Beyond
  5. 2024LoG-VMamba: Local-Global Vision Mamba for Medical Image Segmentation
  6. 2024LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image Segmentation
  7. 2024ProMamba: Prompt-Mamba for polyp segmentation
  8. 2024Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation