Automatic Medical Generated Analysis Based on Histopathology Image Segmentation Half UNet

Abstract

This study reviews and benchmarks SAM2, YOLOv8, UNet, and Half UNet for histopathology image segmentation, integrating outputs with biomedical language models like BioGPT, BioBERT, and DeepSeek VL to generate diagnostic reports. Experiments on the TCGA dataset show that Half UNet offers efficient, accurate segmentation, while SAM2 excels in few-shot settings. Combining segmentation with language models enhances interpretability and automation, improving workflow efficiency and diagnostic accuracy. However, challenges remain in generalizing across tissue types and staining methods. Overall, the integrated approach marks significant progress toward fully automated histopathology analysis.

Introduction

Artificial Intelligence (AI) is transforming histopathology by automating the analysis of complex tissue structures, overcoming the limitations of manual, time-consuming diagnostic methods. This research proposes a modular end-to-end AI framework that combines state-of-the-art segmentation models (SAM2, YOLOv8, UNet, and Half UNet) with advanced biomedical language models (BioGPT, BioBERT, DeepSeek VL) to streamline diagnostic workflows and improve accuracy.

Key Challenges Addressed

• Domain generalization across tissue types and stains.

• High annotation dependency in supervised models.

• Computational load of whole-slide images.

• Stain variability and multi-resolution analysis.

• Interpretability and integration into clinical workflows.

Proposed System Overview

• Segmentation: Half UNet, an optimized version of UNet, reduces parameters by 98.6% while maintaining accuracy. It efficiently identifies tissue regions of interest, such as tumors.

• Language Decoding: BioGPT, BioBERT, and DeepSeek VL interpret segmented images into clinically relevant, structured diagnostic reports, linking visual data with biomedical knowledge.

The architecture is modular, enabling independent optimization of visual and textual components, improving efficiency, interpretability, and diagnostic precision.

Methodology Highlights

• Segmentation Models:

  • UNet: High-resolution biomedical image segmentation via encoder-decoder structure with skip connections.

  • YOLOv8: Real-time instance segmentation with fast inference using backbone, FPN, and head architecture.

  • SAM2: Video-based segmentation with memory and prompt integration.

• Language Models:

  • BioGPT: Transformer for generating biomedical text from image-derived prompts.

  • BioBERT: Pretrained on biomedical literature, ideal for named entity recognition and relation extraction.

  • DeepSeek VL: Multimodal model aligning visual and textual data for tasks like image captioning and diagnosis generation.

Performance Evaluation

• Metrics Used: Accuracy, Precision, Recall, F1 Score, and Loss functions to assess model reliability, especially on class-imbalanced datasets typical in medicine.

• Efficiency: Half UNet significantly reduces computational complexity while preserving segmentation quality, ideal for large datasets like TCGA.

• Results: Masked regions produced by Half UNet align well with diagnostic regions, showing its effectiveness in real-world pathology applications.

Conclusion

The proposed framework demonstrates robust performance in automated histopathology analysis, combining advanced segmentation models with biomedical language processing. SAM2 excels in capturing intricate tissue architecture (e.g., tumor-stroma interfaces) and achieves scale-agnostic feature representation, outperforming threshold-based methods by 12% in Dice scores on TCGA-KICH data. Half UNet balances efficiency and accuracy, reducing training time by 34% compared to UNet while maintaining segmentation precision (F1: 0.9406). Despite SAM2’s challenges with overlapping nuclei in dense tumor regions, Half UNet preserves glandular structures (recall: 0.92). Integration with language models enhances diagnostic utility: BioGPT-generated reports align with histopathological standards in 87% of cases, while DeepSeek-VL improves interpretability, linking segmented regions to molecular profiles with 94.3% concordance. The framework generalizes effectively across five major TCGA cancer types (91% accuracy), with minimal performance drops (<5%) only in rare sarcoma subtypes due to limited training data. By reducing inference time by 28% through optimized architectures (e.g., 512-channel bridge layers) and enabling multimodal analysis, this approach bridges visual and textual data, streamlining pathology workflows. Future work should address rare subtype robustness, stain variability, and further refine visual-textual integration to advance scalable, clinically actionable AI-driven diagnostics.

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Copyright

Copyright © 2025 Veraldo Novaren, Elguerch Badr, Ait Ameur Youssef, Paramaputra Rafi Zahran. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.