Pathologist Attention-Aligned Report Generation for Prostate Histopathology

2026-02-24 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Medical Imaging AI · Depth: Expert, long

Summary

A new framework introduces pathologist attention-aligned report generation for prostate histopathology, leveraging a novel multimodal human-attention dataset. This dataset comprises 121 prostate whole-slide images (WSIs) with pathologists' multi-scale viewport trajectories synchronized with verbal descriptions. The approach fine-tunes two report generation models, HistGen and WSI-LLaVA, using an attention-alignment loss that regularizes model attention to match pathologist attention distribution. Experiments demonstrate average gains of 10.9% on NLP-based metrics and 19.3% in accuracy across five clinically relevant report components. The method also produces model attention maps at inference time that align more closely with pathologist attention, providing stronger visual support and enhancing interpretability with minimal computational overhead and approximately 7 hours of pathologist annotation effort.

Key takeaway

For Machine Learning Engineers developing diagnostic report generation models for histopathology, integrating pathologist attention as an auxiliary training signal can significantly boost model accuracy and interpretability. You should consider collecting expert attention data, even with modest effort, to fine-tune existing vision-language models like HistGen or WSI-LLaVA. This approach improves report quality and provides stronger visual evidence for diagnostic statements.

Key insights

Aligning AI model attention with pathologist visual attention significantly improves prostate cancer report generation and interpretability.

Principles

• Human attention provides strong semantic cues for medical imaging tasks.
• Auxiliary supervision can improve report quality and interpretability.
• Last-layer attention alignment is sufficient and lightweight.

Method

Collected multimodal human-attention dataset from 121 prostate WSIs. Fine-tuned report generation models (HistGen, WSI-LLaVA) using an attention-alignment loss (KL divergence) between model and human attention distributions.

In practice

• Integrate pathologist attention as an auxiliary training signal.
• Use attention-alignment loss for improved report quality.
• Generate visual support maps for enhanced interpretability.

Topics

• Prostate Histopathology
• Pathology Report Generation
• Human Attention Alignment
• Whole-Slide Images
• Vision-Language Models
• Medical AI Interpretability

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.