A multi-task framework for breast cancer segmentation and classification in ultrasound imaging

The experimental findings underscore the efficacy of multi-task techniques, showcasing better generalization capabilities when considering all image types: benign, malignant, and non-tumor images. Consequently, our methodology represents an advance towards more general architectures with real clinical applications in the breast cancer field.

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Publication Details

Journal
Computer Methods and Programs in Biomedicine
Published
2024-12-04
DOI
https://doi.org/10.1016/j.cmpb.2024.108540
Citations
27
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
9.38
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article

A multi-task framework for breast cancer segmentation and classification in ultrasound imaging

Carlos Aumente-Maestro, Jorge Díez, Beatriz Remeseiro
27 citations
Computer Methods and Programs in Biomedicine
AI in cancer detection
9.38
article

A multi-task framework for breast cancer segmentation and classification in ultrasound imaging

Carlos Aumente-Maestro, Jorge Díez, Beatriz Remeseiro
article en
27 citations

Abstract

The experimental findings underscore the efficacy of multi-task techniques, showcasing better generalization capabilities when considering all image types: benign, malignant, and non-tumor images. Consequently, our methodology represents an advance towards more general architectures with real clinical applications in the breast cancer field.

Computer Methods and Programs in BiomedicineVol. 260
Universidad de Oviedo (ES)
Good health and well-being
Openalex Percentile: Top 2%
AI in cancer detection
9.38
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