Evidence map›Paper›PMID 40556771›Full record

ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2024

Semi-Supervised Contrastive VAE for Disentanglement of Digital Pathology Images.

Mahmudul Hasan, Xiaoling Hu, Shahira Abousamra, Prateek Prasanna, Joel Saltz, Chao Chen

Abstract read
In one paragraph

Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. A Topological Comparison of the Fluorescence Imitating Brightfield Imaging and H&E Imaging.Topology- and graph-informed imaging informatics : first International Workshop, TGI3 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings. International Workshop on Topology- and Graph-Informed ... · 2025
    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

6 authors.

Mahmudul HasanStony Brook University, Stony Brook, NY, USA.
Xiaoling HuHarvard Medical School, Boston, MA, USA.
Shahira AbousamraStony Brook University, Stony Brook, NY, USA.
Prateek PrasannaStony Brook University, Stony Brook, NY, USA.
Joel SaltzStony Brook University, Stony Brook, NY, USA.
Chao ChenStony Brook University, Stony Brook, NY, USA.

Funding

Methods and Tools for Integrating Pathomics Data into Cancer RegistriesUH3CA225021 · NCI · STATE UNIVERSITY NEW YORK STONY BROOK · PI CLIFFORD, GARI DAVID, DURBIN, ERIC B. · 2020 to 2022
$1.9M
DMS/NIGMS 1: Topological Study on Histological Images and Spatial TranscriptomicsR01GM148970 · NIGMS · STATE UNIVERSITY NEW YORK STONY BROOK · PI CHEN, CHAO, JOHNSON, TRAVIS STEELE · 2022 to 2024
$682k
IMAT-ITCR Collaboration: Combining FIBI and topological data analysis: Synergistic approaches for tumor structural microenvironment explorationR21CA258493 · NCI · STATE UNIVERSITY NEW YORK STONY BROOK · PI CHEN, CHAO, PRASANNA, PRATEEK · 2022 to 2023
$467k
NCI NIH HHS R21 CA258493NCI NIH HHS UH3 CA225021NIGMS NIH HHS R01 GM148970
6 · The paper itself

Abstract

Despite the strong prediction power of deep learning models, their interpretability remains an important concern. Disentanglement models increase interpretability by decomposing the latent space into interpretable subspaces. In this paper, we propose the first disentanglement method for pathology images. We focus on the task of detecting tumor-infiltrating lymphocytes (TIL). We propose different ideas including cascading disentanglement, novel architecture, and reconstruction branches. We achieve superior performance on complex pathology images, thus improving the interpretability and even generalization power of TIL detection deep learning models. Our codes are available at https://github.com/Shauqi/SS-cVAE.

Indexed as

Contrastive VAEDigital PathologyDisentanglement

Identifiers

PMID40556771
PMCPMC12186872

What OpenQuestion holds

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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.