Beyond Size and Class Balance: Alpha as a New Dataset Quality Metric for Deep Learning.

Josiah Couch, Rima Arnaout, Ramy Arnaout

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In one paragraph

Article in ArXiv, 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

Updated by
Beyond Size and Class Balance: Alpha as a New Dataset Quality Metric for Deep Learning.2026

5 · Who and what money

Authors and funding

3 authors.

Josiah CouchDepartment of Pathology at Beth Israel Deaconess Medical Center (BIDMC), Boston, MA 02215.ORCID https://orcid.org/0000-0002-7416-5858

Rima ArnaoutDepartment of Medicine, the Bakar Institute for Computational Health Sciences, and the Center for Intelligent Imaging at the University of California San Francisco, San Francisco, CA 94143.ORCID https://orcid.org/0000-0002-7134-0040

Ramy ArnaoutDepartment of Pathology and the Division of Clinical Informatics, Department of Medicine, BIDMC and with Harvard Medical School, Boston, MA 02215.ORCID https://orcid.org/0000-0001-6955-9310

Funding

Toward efficient performance for deep learning on medical imagingR01HL150394 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Rima Arnaout · 2020 to 2026

$6.2M

An Integrated Multilevel Modeling Framework for Repertoire-Based DiagnosticsR01AI148747 · NIAID · BETH ISRAEL DEACONESS MEDICAL CENTER · PI ARNAOUT, RAMY · 2020 to 2024

$2.8M

NHLBI NIH HHS R01 HL150394NIAID NIH HHS R01 AI148747

6 · The paper itself

Abstract

In deep learning, achieving high performance on image classification tasks requires diverse training sets. However, the current best practice-maximizing dataset size and class balance-does not guarantee dataset diversity. We hypothesized that, for a given model architecture, model performance can be improved by maximizing diversity more directly. To test this hypothesis, we introduce a comprehensive framework of diversity measures from ecology that generalizes familiar quantities like Shannon entropy by accounting for similarities among images. (Size and class balance emerge as special cases.) Analyzing thousands of subsets from seven medical datasets showed that the best correlates of performance were not size or class balance but

Indexed as

artificial neural networkscomputational cardiologycomputational pathologycomputer visiondataset qualitydiversityentropymedical imagingsimilarity

Identifiers

PMID39830079

PMCPMC11741458

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-SA

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.