ArticleNature communications2025
Deep learning quantifies pathologists' visual patterns for whole slide image diagnosis.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- ConMIL: interactive and contrastive text-guided multiple instance learning for whole slide image classification.Bioinformatics (Oxford, England) · 2026Article
- Swarm intelligence-guided ROI selection for deep learning assessment of HER2 in colorectal cancer.Scientific reports · 2026Article
- Interpretable CRAM‑Enhanced Lightweight Dual‑Branch CNN for Real‑Time Breast Cancer Histopathology in Internet‑of‑Medical‑Things Environments.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Deep learning-driven immunohistochemical analysis of renal lymphatics for chronic kidney disease: bioinformatic and histopathological study.Journal of pathology and translational medicine · 2026Article
- Quantum-enhanced multimodal prognostic transformer for skin disease progression prediction and visualization.Scientific reports · 2026Article
- Article
- Explainable federated transformer framework for joint leukemia classification and stage prediction.Scientific reports · 2026Article
- SpatialFinder: a human-in-the-loop vision-language framework for prioritizing high-value regions in spatial transcriptomics.Frontiers in bioinformatics · 2026Article
- Psoriasis as a systemic inflammatory disease: an immune set-point framework for comorbidities and relapse.Frontiers in immunology · 2026Review
- Decision Support Systems in Neurosurgery: Current Applications and Future Directions.Sensors (Basel, Switzerland) · 2025Review
- Artificial Intelligence in Postmenopausal Health: From Risk Prediction to Holistic Care.Journal of clinical medicine · 2025Review
- Chemoarchitectural studies of the rat hypothalamus and zona incerta.bioRxiv : the preprint server for biology · 2025Article
- An Explainable Radiomics-Based Classification Model for Sarcoma Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- 1DCNN-BiLSTM-transformer hypertension risk prediction model based on APW.Frontiers in microbiology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Based on the expertise of pathologists, the pixelwise manual annotation has provided substantial support for training deep learning models of whole slide images (WSI)-assisted diagnostic. However, the collection of pixelwise annotation demands massive annotation time from pathologists, leading to a high burden of medical manpower resources, hindering to construct larger datasets and more precise diagnostic models. To obtain pathologists' expertise with minimal pathologist workloads then achieve precise diagnostics, we collect the image review patterns of pathologists by eye-tracking devices. Simultaneously, we design a deep learning system: Pathology Expertise Acquisition Network (PEAN), based on the collected visual patterns, which can decode pathologists' expertise and then diagnose WSIs. Eye-trackers reduce the time required for annotating WSIs to 4%, of the manual annotation. We evaluate PEAN on 5881 WSIs and 5 categories of skin lesions, achieving a high area under the curve of 0.992 and an accuracy of 96.3% on diagnostic prediction. This study fills the gap in existing models' inability to learn from the diagnostic processes of pathologists. Its efficient data annotation and precise diagnostics provide assistance in both large-scale data collection and clinical care.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.