Unit-Based Histopathology Tissue Segmentation via Multi-Level Feature Representation

2025/07/16 by Ashkan Shakarami, Azade Farshad, Shakarami, Ashkan +11 · 1 citation
Computer Science · Medicine · #AI in cancer detection #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #Digital Imaging for Blood Diseases #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Radiomics and Machine Learning in Medical Imaging #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2507.12427

openalex publication_date 2025/07/16 · openalex created_date 2025/10/14 · openalex updated_date 2026/07/28

Abstract

We propose UTS, a unit-based tissue segmentation framework for histopathology that classifies each fixed-size 32 * 32 tile, rather than each pixel, as the segmentation unit. This approach reduces annotation effort and improves computational efficiency without compromising accuracy. To implement this approach, we introduce a Multi-Level Vision Transformer (L-ViT), which benefits the multi-level feature representation to capture both fine-grained morphology and global tissue context. Trained to segment breast tissue into three categories (infiltrating tumor, non-neoplastic stroma, and fat), UTS supports clinically relevant tasks such as tumor-stroma quantification and surgical margin assessment. Evaluated on 386,371 tiles from 459 H&E-stained regions, it outperforms U-Net variants and transformer-based baselines. Code and Dataset will be available at GitHub.

Citations

• Restoring Images in Adverse Weather Conditions via Histogram Transformer
• Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation
• Swin-Unet: Unet-Like Pure Transformer for Medical Image Segmentation
• TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
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• CBAM: Convolutional Block Attention Module
• Attention U-Net: Learning Where to Look for the Pancreas
• Squeeze-and-Excitation Networks
• DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
• DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
• Deep Residual Learning for Image Recognition
• U-Net: Convolutional Networks for Biomedical Image Segmentation

Cited by

• Stress-Aware Resilient Neural Training

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