Evidence map›Paper›PMID 38827244›Full record

ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2023

Distilling BlackBox to Interpretable Models for Efficient Transfer Learning.

Shantanu Ghosh, Ke Yu, Kayhan Batmanghelich

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 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

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

3 authors.

Shantanu GhoshDepartment of Electrical and Computer Engineering, Boston University, Boston, MA, USA.ORCID 0000-0003-4085-541X
Ke YuIntelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA.
Kayhan BatmanghelichDepartment of Electrical and Computer Engineering, Boston University, Boston, MA, USA.

Funding

Integrating Multi-Modal Data and Biomechanics in COPD: Toward Robust and Interpretable Biomarkers for Disease Subtyping and ProgressionR01HL141813 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Kayhan Batmanghelich · 2018 to 2026
$4.2M
NHLBI NIH HHS R01 HL141813
6 · The paper itself

Abstract

Building generalizable AI models is one of the primary challenges in the healthcare domain. While radiologists rely on generalizable descriptive rules of abnormality, Neural Network (NN) models suffer even with a slight shift in input distribution (

Indexed as

Explainable-AIInterpretable modelsTransfer learning

Identifiers

PMID38827244
PMCPMC11141113

What OpenQuestion holds

Textmetadata
LicenceTDM
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.