Evaluating Recalibrating AI Models for Breast Cancer Diagnosis in a New Context: Insights from Transfer Learning, Image Enhancement and High-Quality Training Data Integration.

Zhengqiang Jiang, Ziba Gandomkar, Phuong Dung Yun Trieu, Seyedamir Tavakoli Taba, Melissa L Barron, Peyman Obeidy, Sarah J Lewis

Abstract read

PubMed Full text at PMC Publisher

In one paragraph

Article in Cancers, 2024. 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.

Cohort Profile: Lifepool.International journal of epidemiology · 2025
Article
Optimised Convolution Layers of DnCNN using Vedic Multiplier and Hyperparameter Tuning in Cancer Detection on Field Programmable Gate ArrayCurrent medical imaging · 2025
Article
Comparing Radiologists' and Artificial Intelligence Performance Detecting Suspicious Microcalcifications on Screening Mammograms: A Pilot Cross-Sectional Study.Cancer control : journal of the Moffitt Cancer Center
Article

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

7 authors.

Zhengqiang JiangDiscipline of Medical Imaging Science, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia.

Ziba GandomkarDiscipline of Medical Imaging Science, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia.

Phuong Dung Yun TrieuDiscipline of Medical Imaging Science, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia.ORCID 0000-0001-7021-6331

Seyedamir Tavakoli TabaDiscipline of Medical Imaging Science, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia.

Melissa L BarronDiscipline of Medical Imaging Science, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia.ORCID 0000-0002-1143-5486

Peyman ObeidyDiscipline of Medical Imaging Science, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia.

Sarah J LewisDiscipline of Medical Imaging Science, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia.ORCID 0000-0002-4791-9845

Funding

National Breast Cancer Foundation IIRS-22-087

6 · The paper itself

Abstract

This paper investigates the adaptability of four state-of-the-art artificial intelligence (AI) models to the Australian mammographic context through transfer learning, explores the impact of image enhancement on model performance and analyses the relationship between AI outputs and histopathological features for clinical relevance and accuracy assessment. A total of 1712 screening mammograms (

Indexed as

artificial intelligencedeep learningimage enhancementmammographyradiologists’ concordancesaliency mapstransfer learning

Identifiers

PMID38254813

DOI10.3390/cancers16020322

PMCPMC10814142

What OpenQuestion holds

Textmetadata

LicenceCC BY

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.