Evidence map›Paper›PMID 37829549›Full record

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

How Does Pruning Impact Long-Tailed Multi-label Medical Image Classifiers?

Gregory Holste, Ziyu Jiang, Ajay Jaiswal, Maria Hanna, Shlomo Minkowitz, Alan C Legasto, Joanna G Escalon, Sharon Steinberger, Mark Bittman, Thomas C Shen and 5 more

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, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

Gregory HolsteThe University of Texas at Austin, Austin, TX, USA.
Ziyu JiangTexas A&M University, College Station, TX, USA.
Ajay JaiswalThe University of Texas at Austin, Austin, TX, USA.
Maria HannaWeill Cornell Medicine, New York, NY, USA.
Shlomo MinkowitzWeill Cornell Medicine, New York, NY, USA.
Alan C LegastoWeill Cornell Medicine, New York, NY, USA.
Joanna G EscalonWeill Cornell Medicine, New York, NY, USA.
Sharon SteinbergerWeill Cornell Medicine, New York, NY, USA.
Mark BittmanWeill Cornell Medicine, New York, NY, USA.
Thomas C ShenClinical Center, National Institutes of Health, Bethesda, MD, USA.
Ying DingThe University of Texas at Austin, Austin, TX, USA.
Ronald M SummersClinical Center, National Institutes of Health, Bethesda, MD, USA.
George ShihWeill Cornell Medicine, New York, NY, USA.
Yifan PengWeill Cornell Medicine, New York, NY, USA.
Zhangyang WangThe University of Texas at Austin, Austin, TX, USA.

Funding

A framework to enhance radiology structured report by invoking NLP and DL: Models and ApplicationsR00LM013001 · NLM · WEILL MEDICAL COLL OF CORNELL UNIV · PI PENG, YIFAN · 2020 to 2022
$710k
Achieving Model Fairness on Automatic Primary Open-angle Glaucoma ScreeningR21EY035296 · NEI · WEILL MEDICAL COLL OF CORNELL UNIV · PI PENG, YIFAN · 2023 to 2023
$466k
NEI NIH HHS R21 EY035296NLM NIH HHS R00 LM013001
6 · The paper itself

Abstract

Pruning has emerged as a powerful technique for compressing deep neural networks, reducing memory usage and inference time without significantly affecting overall performance. However, the nuanced ways in which pruning impacts model behavior are not well understood, particularly for

Indexed as

Chest X-RayImbalanceLong-Tailed LearningPruning

Identifiers

PMID37829549
PMCPMC10568970

What Socratic 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 Socratic graph.