Deep Learning and Few-Shot Learning in the Detection of Skin Cancer: An Overview

Akinrinade, Olusoji B.; Du, Chunglin; Ajila, Samuel; Olowookere, Toluwase Ayobami

doi:10.1007/978-3-031-18461-1_18

Cited by 1 publication

(2 citation statements)

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“…The detection of rare cancers is particularly critical due to the scarcity of data. Several studies have employed few-shot learning to address rare cancer detection [2,119]. However, acquiring a large amount of auxiliary data from the same distribution as the target data is often challenging, necessitating the use of CDFSL in rare cancer detection.…”

Section: Applicationsmentioning

confidence: 99%

“…In addition, we define CDFSL using the machine learning definition [66,68] and transfer learning theory [98]. Secondly, the analysis of a large number of related papers shows that the unique issue of CDFSL is the unreliable two-stage empirical risk minimization problem, which stems from the combination of two factors: (1) a significant discrepancy between the source and target domains(both in terms of the tasks they perform and the domains themselves), (2) the limited amount of supervised information available in the target domain. The details are discussed in Section 2.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey

Xu¹,

Zhi²,

Sun³

et al. 2023

Preprint

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Deep learning has been highly successful in computer vision with large amounts of labeled data, but struggles with limited labeled training data. To address this, Few-shot learning (FSL) is proposed, but it assumes that all samples (including source and target task data, where target tasks are performed with prior knowledge from source ones) are from the same domain, which is a stringent assumption in the real world. To alleviate this limitation, Cross-domain few-shot learning (CDFSL) has gained attention as it allows source and target data from different domains and label spaces. This paper provides a comprehensive review of CDFSL at the first time, which has received far less attention than FSL due to its unique setup and difficulties. We expect this paper to serve as both a position paper and a tutorial for those doing research in CDFSL. This review first introduces the definition of CDFSL and the issues involved, followed by the core scientific question and challenge. A comprehensive review of validated CDFSL approaches from the existing literature is then presented, along with their detailed descriptions based on a rigorous taxonomy. Furthermore, this paper outlines and discusses several promising directions of CDFSL that deserve further scientific investigation, covering aspects of problem setups, applications and theories.

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Section: Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%