Evidence map›Paper›PMID 40070646›Full record

ArticleFrontiers in medicine2025

Abnormality-aware multimodal learning for WSI classification.

Thao M Dang, Qifeng Zhou, Yuzhi Guo, Hehuan Ma, Saiyang Na, Thao Bich Dang, Jean Gao, Junzhou Huang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Thao M DangDepartment of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, United States.
Qifeng ZhouDepartment of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, United States.
Yuzhi GuoDepartment of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, United States.
Hehuan MaDepartment of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, United States.
Saiyang NaDepartment of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, United States.
Thao Bich DangDepartment of Pulmonary and Critical Care, University of Arizona, Phoenix, AZ, United States.
Jean GaoDepartment of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, United States.
Junzhou HuangDepartment of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Whole slide images (WSIs) play a vital role in cancer diagnosis and prognosis. However, their gigapixel resolution, lack of pixel-level annotations, and reliance on unimodal visual data present challenges for accurate and efficient computational analysis. Existing methods typically divide WSIs into thousands of patches, which increases computational demands and makes it challenging to effectively focus on diagnostically relevant regions. Furthermore, these methods frequently rely on feature extractors pretrained on natural images, which are not optimized for pathology tasks, and overlook multimodal data sources such as cellular and textual information that can provide critical insights. To address these limitations, we propose the

Indexed as

abnormal detectionfoundation modelGaussian Mixture Variational Autoencodermultimodal fusionWSI analysis

Identifiers

PMID40070646
PMCPMC11893561

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.