Evidence map›Paper›PMID 37547660›Full record

ArticleArXiv2023

Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples.

Andrew H Song, Mane Williams, Drew F K Williamson, Guillaume Jaume, Andrew Zhang, Bowen Chen, Robert Serafin, Jonathan T C Liu, Alex Baras, Anil V Parwani and 1 more

Abstract readPreprint
PubMed Full text at PMC
In one paragraph

Article in ArXiv, 2023. 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

5 · Who and what money

Authors and funding

11 authors.

Andrew H SongDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Mane WilliamsDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Drew F K WilliamsonDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Guillaume JaumeDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Andrew ZhangHarvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA.
Bowen ChenDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Robert SerafinDepartment of Mechanical Engineering, Bioengineering, and Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA.
Jonathan T C LiuDepartment of Mechanical Engineering, Bioengineering, and Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA.
Alex BarasDepartment of Pathology, Johns Hopkins Hospital, Baltimore, MD, USA.
Anil V ParwaniDepartment of Pathology, The Ohio State University, Columbus, Ohio, USA.
Faisal MahmoodDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

Funding

Training Program in Bioinformatics and Integrative GenomicsT32HG002295 · NHGRI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Peter J Park · 2001 to 2026
$15.8M
Multimodal and Generative AI for PathologyR35GM138216 · NIGMS · BROAD INSTITUTE, INC. · PI Faisal Mahmood · 2020 to 2026
$3.4M
Prostate cancer risk stratification via computational 3D pathologyR01CA268207 · NCI · UNIVERSITY OF WASHINGTON · PI Jonathan T.C. Liu, Anant Madabhushi · 2022 to 2026
$3.1M
Oncopathology Training ProgramT32CA251062 · NCI · DANA-FARBER CANCER INST · PI JON C. ASTER, KATHLEEN H BURNS · 2020 to 2026
$2.4M
Instrumentation platform for 3D pathology with open-top light-sheet microscopyR01EB031002 · NIBIB · UNIVERSITY OF WASHINGTON · PI LIU, JONATHAN T.C. · 2021 to 2024
$1.9M
Acquisition of a Sub-micron High-Resolution 3D X-Ray Imaging SystemS10OD023519 · OD · HARVARD UNIVERSITY · PI BELL, DAVID CHARLES · 2018 to 2018
$1.8M
NCI NIH HHS R01 CA268207NCI NIH HHS T32 CA251062NHGRI NIH HHS T32 HG002295NIBIB NIH HHS R01 EB031002NIGMS NIH HHS R35 GM138216NIH HHS S10 OD023519
6 · The paper itself

Abstract

Human tissue consists of complex structures that display a diversity of morphologies, forming a tissue microenvironment that is, by nature, three-dimensional (3D). However, the current standard-of-care involves slicing 3D tissue specimens into two-dimensional (2D) sections and selecting a few for microscopic evaluation

Identifiers

PMID37547660
PMCPMC10402184

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.