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Deep Learning With Limited Labeled D...

Huang, Zhe,

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Deep Learning With Limited Labeled Data: New Methods and Applications to Echocardiography /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep Learning With Limited Labeled Data: New Methods and Applications to Echocardiography // Zhe Huang.
作者:	Huang, Zhe,
面頁冊數:	1 electronic resource (230 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
Contained By:	Dissertations Abstracts International86-03B.
標題:	Computer science. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31556943
ISBN:	9798384086307

Deep Learning With Limited Labeled Data: New Methods and Applications to Echocardiography /
Huang, Zhe,

Deep Learning With Limited Labeled Data: New Methods and Applications to Echocardiography /Zhe Huang. - 1 electronic resource (230 pages)

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

Deep neural networks have revolutionized various fields via high-quality flexible predictions. However, their effectiveness often hinges on the availability of large labeled datasets for training-a limitation that is particularly pronounced in medical applications. This thesis addresses the challenge of learning from limited labeled data in three complementary areas:1. Dataset Development and Realistic Evaluation: We developed the Tufts Echocardiogram Datasets (TMED) (Huang et al., 2021b, 2022a). This dataset serves dual purposes: providing an authentic benchmark for semi-supervised learning (SSL) method development and encouraging effort toward a clinically impactful problem: diagnosing a heart disease known as Aortic Stenosis. In later work (Huang et al., 2024a), we developed a comprehensive benchmark of semi-supervised and self-supervised methods across four medical imaging tasks, helping practitioners with limited labeling and compute budget to achieve good results for tasks at hand.2.Method Development: We introduced two innovative approaches for semi-supervised learning. Fix-A-Step (Huang et al., 2023b) challenges traditional views on out-of-distribution unlabeled data and proposes a simple-to-implement learning procedure that leverages beneficial aspects of such data while guarding against harmful effects. InterLUDE (Huang et al., 2024c) addresses the disconnect between labeled and unlabeled data sets prevalent in current SSL formulations, proposing a novel approach that enhances SSL through beneficial interactions between labeled and unlabeled data.3.Clinical Translation: Focusing on the automated screening of Aortic Stenosis (AS), we conducted a proof-of-concept study (Wessler et al., 2023) demonstrating the feasibility of using deep learning for AS detection. Further studies improved the model by adopting a Multiple-Instance Learning (MIL) framework (Huang et al., 2023d) and incorporating multiple imaging modalities used in clinical practice (Huang et al., 2024b), significantly advancing AS diagnosis performance.

English

ISBN: 9798384086307Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Semi-supervised learning

Deep Learning With Limited Labeled Data: New Methods and Applications to Echocardiography /
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