Pathologist Attention-Aligned Report Generation for Prostate Histopathology
This work introduces pathologist attention into report generation model training. A multimodal dataset of 121 prostate WSIs with pathologists' gaze, verbal descriptions, and cursor movements was collected. Two models fine-tuned with an attention-alignment loss showed average gains of 10.9% on NLP metrics and 19.3% accuracy across five clinical report components.
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[Submitted on 21 Jul 2026]
Title:Pathologist Attention-Aligned Report Generation for Prostate Histopathology
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Abstract:The allocation of visual attention by pathologists during cancer diagnosis is a highly selective process that critically shapes the information extracted from whole-slide images (WSIs). Human attention helps medical imaging tasks such as classification and segmentation, and becomes a strong semantic cue for identifying diagnostically informative regions for report generation. In this paper, we introduce human attention into the training of pathologist report generation models. To this end, we collected a multimodal human-attention dataset of 121 prostate WSIs annotated with pathologists' multi-scale viewport trajectories synchronized with the pathologists' verbal descriptions and cursor movements for five clinically relevant components (e.g., Gleason patterns). Using this dataset, we finetune two report generation models with an attention-alignment loss that regularizes the model attention over image patches to match the distribution of pathologist attention. We evaluate our approach on prostate cancer report generation and visual question answering using two models with different internal attention mechanisms (i.e., how image tokens are integrated into the language decoder). Experiments show average gains of 10.9% on NLP-based metrics and 19.3% in accuracy across five clinically relevant report components. Further, model attention maps extracted at inference time, with minimal computational overhead, align more closely with pathologist attention, providing stronger visual support for the generated reports by highlighting the regions that most influence the output.
Comments: 11 pages, 4 figures, accepted for publication at the 29th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2026)
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.19624 [cs.CV]
(or arXiv:2607.19624v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.19624
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ruoyu Xue [view email] [v1] Tue, 21 Jul 2026 23:08:20 UTC (3,178 KB)
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