Cross-Domain Few-Shot Sparse-Quantization Aware Learning for Lymphoblast Detection in Blood Smear Images

Dina Aboutahoun*, Rami Zewail, Keiji Kimura, Mostafa I. Soliman

*この研究の対応する著者

研究成果: Conference contribution

抄録

Deep learning for medical image classification has enjoyed increased attention. However, a bottleneck that prevents it from widespread adoption is its dependency on very large, annotated datasets, a condition that cannot always be satisfied. Few-shot learning in the medical domain is still in its infancy but has the potential to overcome these challenges. Compression is a way for models to be deployed on resource-constrained machines. In an attempt to tackle some of the challenges imposed by limited data and high computational resources, we present a few-shot sparse-quantization aware meta-training framework (FS-SQAM). The proposed framework aims to exploit the role of sparsity and quantization for improved adaptability in a low-resource cross-domain setting for the classification of acute lymphocytic leukemia (ALL) in blood cell images. Combining these strategies enables us to approach two of the most common problems that encounter deep learning for medical images: the need for extremely large datasets and high computational resources. Extensive experiments have been conducted to evaluate the performance of the proposed framework on the ALL-IDB2 dataset in a cross-domain few-shot setting. Performance gains in terms of accuracy and compression have been demonstrated, thus serving to realize the suitability of meta-learning on resource-constrained devices. Future advancements in the domain of efficient deep learning computer-aided diagnosis systems will facilitate their adoption in clinical medicine.

本文言語English
ホスト出版物のタイトルPattern Recognition - 7th Asian Conference, ACPR 2023, Proceedings
編集者Huimin Lu, Michael Blumenstein, Sung-Bae Cho, Cheng-Lin Liu, Yasushi Yagi, Tohru Kamiya
出版社Springer Science and Business Media Deutschland GmbH
ページ213-226
ページ数14
ISBN(印刷版)9783031476648
DOI
出版ステータスPublished - 2023
イベント7th Asian Conference on Pattern Recognition, ACPR 2023 - Kitakyushu, Japan
継続期間: 2023 11月 52023 11月 8

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
14408 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference7th Asian Conference on Pattern Recognition, ACPR 2023
国/地域Japan
CityKitakyushu
Period23/11/523/11/8

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータサイエンス一般

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