A Review of Machine Learning Techniques in the Medical Domain

Abstract

We have witnessed a rapid exponential growth of all types of data in all domains specifically in the medical domain. The utilization of machine learning techniques has made significant strides across various domains, with deep learning achieving notable success in recent years. Lately, deep learning has gained increasing attention in the medical field. While deep learning excels at automatically learning discriminative features from raw data, it is still challenging to achieve high performance without a huge amount of data and some handcrafted steps. To address these challenges, deep learning has been incorporated with other new trends and domain knowledge to enhance deep learning's capabilities and improve performance covering the ever-growing needs. Transfer learning utilizes knowledge from natural images, curriculum learning integrates domain-specific knowledge, active learning selects the most informative samples to reduce reliance on labeled data, and federated learning enables collaborative training across organizations while ensuring data privacy. In this review paper, these new trends incorporated with deep learning have been investigated and presented as applications in the medical domain by investigating articles that have applied these trends and published in highly reputable journals in the Science Direct database in recent years.