Evidence map›Paper›PMID 42186543›Full record

ArticleFrontiers in bioinformatics2026

Weak-to-strong generalization enables fully automated training of multi-head mask-RCNN model for segmenting densely overlapping cell nuclei in multiplex whole-slice brain images.

Lin Bai, Xiaoyang Li, Liqiang Huang, Quynh Nguyen, Hien Van Nguyen, Saurabh Prasad, Dragan Maric, John Redell, Pramod Dash, Badrinath Roysam

Abstract read
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In one paragraph

Article in Frontiers in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Lin BaiDepartment of Electrical and Computer Engineering, University of Houston, Houston, TX, United States.
Xiaoyang LiDepartment of Electrical and Computer Engineering, University of Houston, Houston, TX, United States.
Liqiang HuangDepartment of Electrical and Computer Engineering, University of Houston, Houston, TX, United States.
Quynh NguyenDepartment of Electrical and Computer Engineering, University of Houston, Houston, TX, United States.
Hien Van NguyenDepartment of Electrical and Computer Engineering, University of Houston, Houston, TX, United States.
Saurabh PrasadDepartment of Electrical and Computer Engineering, University of Houston, Houston, TX, United States.
Dragan MaricNational Institute of Neurological Disorders and Stroke, Bethesda, MD, United States.
John RedellDepartment of Neurobiology and Anatomy, The University of Texas McGovern Medical School, Houston, TX, United States.
Pramod DashDepartment of Neurobiology and Anatomy, The University of Texas McGovern Medical School, Houston, TX, United States.
Badrinath RoysamDepartment of Electrical and Computer Engineering, University of Houston, Houston, TX, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present a weak to strong generalization methodology for fully automated training of a multi-head extension of the Mask-RCNN method with efficient channel attention for reliable segmentation of overlapping cell nuclei in multiplex cyclic immunofluorescent (IF) whole-slide images (WSIs), and present evidence for pseudo-label correction and coverage expansion, the key phenomena underlying weak to strong generalization. This method is designed to enable domain adaptation for multiplex spatial proteomics imaging data, eliminating the need for additional human annotations in the target domain. We also present metrics for automated self-diagnosis of segmentation quality in production environments, where human visual proofreading of massive WSI images is unaffordable. Our method was benchmarked against five current widely used methods and showed a significant improvement. The code, sample WSI images, and high-resolution segmentation results are provided in open form for community adoption and adaptation.

Indexed as

automated trainingcell nucleiinstance segmentationmultiplex immunofluorescenceweak to strong learningwhole-slide imaging

Identifiers

PMID42186543
PMCPMC13198928

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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.