Evidence map›Paper›PMID 39553831›Full record

ArticleConference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops2024

Uncertainty Estimation for Tumor Prediction with Unlabeled Data.

Juyoung Yun, Shahira Abousamra, Chen Li, Rajarsi Gupta, Tahsin Kurc, Dimitris Samaras, Alison Van Dyke, Joel Saltz, Chao Chen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops, 2024. 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

9 authors.

Juyoung YunStony Brook University, Department of Computer Science, USA.
Shahira AbousamraStony Brook University, Department of Computer Science, USA.
Chen LiStony Brook University, Department of Computer Science, USA.
Rajarsi GuptaStony Brook University, Department of Biomedical Informatics, USA.
Tahsin KurcStony Brook University, Department of Biomedical Informatics, USA.
Dimitris SamarasStony Brook University, Department of Computer Science, USA.
Alison Van DykeNational Cancer Institute, Maryland, USA.
Joel SaltzStony Brook University, Department of Biomedical Informatics, USA.
Chao ChenStony Brook University, Department of Biomedical Informatics, 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
NCI NIH HHS UH3 CA225021NIGMS NIH HHS R01 GM148970
6 · The paper itself

Abstract

Estimating uncertainty of a neural network is crucial in providing transparency and trustworthiness. In this paper, we focus on uncertainty estimation for digital pathology prediction models. To explore the large amount of unlabeled data in digital pathology, we propose to adopt novel learning method that can fully exploit unlabeled data. The proposed method achieves superior performance compared with different baselines including the celebrated Monte-Carlo Dropout. Closeup inspection of uncertain regions reveal insight into the model and improves the trustworthiness of the models.

Identifiers

PMID39553831
PMCPMC11567674

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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.