Transfer Learning in Breast Cancer Diagnoses via Ultrasound Imaging

Transfer learning is a machine learning approach that reuses a learning method developed for a task as the starting point for a model on a target task. The goal of transfer learning is to improve performance of target learners by transferring the knowledge contained in other (but related) source dom...

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Vydáno v:Cancers Ročník 13; číslo 4; s. 738
Hlavní autoři: Ayana, Gelan, Dese, Kokeb, Choe, Se-woon
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI 10.02.2021
Témata:
Review
transfer learning
breast cancer
ultrasound
ISSN:2072-6694, 2072-6694
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Shrnutí:Transfer learning is a machine learning approach that reuses a learning method developed for a task as the starting point for a model on a target task. The goal of transfer learning is to improve performance of target learners by transferring the knowledge contained in other (but related) source domains. As a result, the need for large numbers of target-domain data is lowered for constructing target learners. Due to this immense property, transfer learning techniques are frequently used in ultrasound breast cancer image analyses. In this review, we focus on transfer learning methods applied on ultrasound breast image classification and detection from the perspective of transfer learning approaches, pre-processing, pre-training models, and convolutional neural network (CNN) models. Finally, comparison of different works is carried out, and challenges—as well as outlooks—are discussed.
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ISSN:2072-6694
2072-6694
DOI:10.3390/cancers13040738