Few Shot Learning for Medical Imaging: A Review of Categorized Images

Hasan Md Imran; Tareque Abu Abdullah; Suriya Islam Chowdhury; Md Alamin; Muhammad Asad

doi:10.1109/CICT59886.2023.10455365

Few Shot Learning for Medical Imaging: A Review of Categorized Images

Hasan Md Imran^*, Tareque Abu Abdullah, Suriya Islam Chowdhury, Md Alamin, Muhammad Asad

^*Corresponding author for this work

School of Engineering, Computing and Mathematics

Clark Atlanta University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Deep learning systems have advanced significantly in numerous medical applications, improving various aspects of patient care. However, they still need to work on the issue of dependence on the availability of training data. Few-shot learning (FSL) is a topic of active study that aims to overcome this limitation. FSL techniques require only a few labeled examples for training. FSL-based Medical Imaging (MI) approaches show great potential because many unknown rare diseases have limited annotated imaging data in the real world. In this study, we conducted a systematic review to discover the state of FSL techniques for medical images. We categorized different types of images, such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI), tissues, and other images.

Original language	English
Title of host publication	2023 IEEE 7th Conference on Information and Communication Technology, CICT 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350305173
DOIs	https://doi.org/10.1109/CICT59886.2023.10455365
Publication status	Published - 2023
Event	7th IEEE Conference on Information and Communication Technology, CICT 2023 - Jabalpur, India Duration: 15 Dec 2023 → 17 Dec 2023

Publication series

Name	2023 IEEE 7th Conference on Information and Communication Technology, CICT 2023

Conference

Conference	7th IEEE Conference on Information and Communication Technology, CICT 2023
Country/Territory	India
City	Jabalpur
Period	15/12/23 → 17/12/23

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition
Hardware and Architecture
Information Systems
Instrumentation

Keywords

few-shot learning
Medical image
meta learning
metric-learner
transfer learning

Access to Document

10.1109/CICT59886.2023.10455365

Cite this

Imran, H. M., Abdullah, T. A., Chowdhury, S. I., Alamin, M., & Asad, M. (2023). Few Shot Learning for Medical Imaging: A Review of Categorized Images. In 2023 IEEE 7th Conference on Information and Communication Technology, CICT 2023 (2023 IEEE 7th Conference on Information and Communication Technology, CICT 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CICT59886.2023.10455365

Imran, Hasan Md ; Abdullah, Tareque Abu ; Chowdhury, Suriya Islam et al. / Few Shot Learning for Medical Imaging : A Review of Categorized Images. 2023 IEEE 7th Conference on Information and Communication Technology, CICT 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE 7th Conference on Information and Communication Technology, CICT 2023).

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abstract = "Deep learning systems have advanced significantly in numerous medical applications, improving various aspects of patient care. However, they still need to work on the issue of dependence on the availability of training data. Few-shot learning (FSL) is a topic of active study that aims to overcome this limitation. FSL techniques require only a few labeled examples for training. FSL-based Medical Imaging (MI) approaches show great potential because many unknown rare diseases have limited annotated imaging data in the real world. In this study, we conducted a systematic review to discover the state of FSL techniques for medical images. We categorized different types of images, such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI), tissues, and other images.",

keywords = "few-shot learning, Medical image, meta learning, metric-learner, transfer learning",

author = "Imran, {Hasan Md} and Abdullah, {Tareque Abu} and Chowdhury, {Suriya Islam} and Md Alamin and Muhammad Asad",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 7th IEEE Conference on Information and Communication Technology, CICT 2023 ; Conference date: 15-12-2023 Through 17-12-2023",

year = "2023",

doi = "10.1109/CICT59886.2023.10455365",

language = "English",

series = "2023 IEEE 7th Conference on Information and Communication Technology, CICT 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2023 IEEE 7th Conference on Information and Communication Technology, CICT 2023",

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}

Imran, HM, Abdullah, TA, Chowdhury, SI, Alamin, M & Asad, M 2023, Few Shot Learning for Medical Imaging: A Review of Categorized Images. in 2023 IEEE 7th Conference on Information and Communication Technology, CICT 2023. 2023 IEEE 7th Conference on Information and Communication Technology, CICT 2023, Institute of Electrical and Electronics Engineers Inc., 7th IEEE Conference on Information and Communication Technology, CICT 2023, Jabalpur, India, 15/12/23. https://doi.org/10.1109/CICT59886.2023.10455365

Few Shot Learning for Medical Imaging: A Review of Categorized Images. / Imran, Hasan Md; Abdullah, Tareque Abu; Chowdhury, Suriya Islam et al.
2023 IEEE 7th Conference on Information and Communication Technology, CICT 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE 7th Conference on Information and Communication Technology, CICT 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Deep learning systems have advanced significantly in numerous medical applications, improving various aspects of patient care. However, they still need to work on the issue of dependence on the availability of training data. Few-shot learning (FSL) is a topic of active study that aims to overcome this limitation. FSL techniques require only a few labeled examples for training. FSL-based Medical Imaging (MI) approaches show great potential because many unknown rare diseases have limited annotated imaging data in the real world. In this study, we conducted a systematic review to discover the state of FSL techniques for medical images. We categorized different types of images, such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI), tissues, and other images.

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Imran HM, Abdullah TA, Chowdhury SI, Alamin M, Asad M. Few Shot Learning for Medical Imaging: A Review of Categorized Images. In 2023 IEEE 7th Conference on Information and Communication Technology, CICT 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 IEEE 7th Conference on Information and Communication Technology, CICT 2023). doi: 10.1109/CICT59886.2023.10455365

Few Shot Learning for Medical Imaging: A Review of Categorized Images

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Keywords

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