Evidence map›Paper›PMID 39632974›Full record

ArticleCommunications biology2024

Task-driven framework using large models for digital pathology.

Jiahui Yu, Tianyu Ma, Feng Chen, Jing Zhang, Yingke Xu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jiahui YuDepartment of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Extreme Photonics and Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China.
Tianyu MaInnovation Center for Smart Medical Technologies and Devices, Binjiang Institute of Zhejiang University, Hangzhou, China.ORCID 0000-0003-3260-7125
Feng ChenDepartment of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Jing ZhangDepartment of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Yingke XuDepartment of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Extreme Photonics and Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China. yingkexu@zju.edu.cn.ORCID 0000-0002-8317-0608

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Microscopy is an indispensable tool for collecting biomedical information in pathological diagnosis, but manual annotation, measurement and interpretation are labor-intensive and costly. Here, we propose a task-driven framework powered by large models that excel in visual analysis and real-time control, paving the way for the next generation of microscopes. We achieve proof-of-concept success on clinical tasks, specifically in adaptive analysis of H&E-stained liver tissue slides. This work demonstrates the advanced capabilities for future digital pathology, setting a new standard for intelligent, efficient, and real-time analysis in clinical applications.

Indexed as

MicroscopyHumansImage Processing, Computer-AssistedLiver

Identifiers

PMID39632974
PMCPMC11618297

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Registered trials

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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.