Related papers: Interpretable Medical Imagery Diagnosis with Self-Attentive Transformers: A Review of Explainable AI for Health Care

Interpretable Medical Imagery Diagnosis with Self-Attentive Transformers: A Review of Explainable AI for Health Care

URL: http://arxiv.org/abs/2309.00252v1
Date: Fri, 1 Sep 2023 05:01:52 GMT
Title: Interpretable Medical Imagery Diagnosis with Self-Attentive Transformers: A Review of Explainable AI for Health Care
Authors: Tin Lai
Abstract summary: Vision Transformers (ViT) have emerged as state-of-the-art computer vision models, benefiting from self-attention modules. Deep-learning models are complex and are often treated as a "black box" that can cause uncertainty regarding how they operate. This review summarises recent ViT advancements and interpretative approaches to understanding the decision-making process of ViT.
Score: 2.7195102129095003
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Recent advancements in artificial intelligence (AI) have facilitated its widespread adoption in primary medical services, addressing the demand-supply imbalance in healthcare. Vision Transformers (ViT) have emerged as state-of-the-art computer vision models, benefiting from self-attention modules. However, compared to traditional machine-learning approaches, deep-learning models are complex and are often treated as a "black box" that can cause uncertainty regarding how they operate. Explainable Artificial Intelligence (XAI) refers to methods that explain and interpret machine learning models' inner workings and how they come to decisions, which is especially important in the medical domain to guide the healthcare decision-making process. This review summarises recent ViT advancements and interpretative approaches to understanding the decision-making process of ViT, enabling transparency in medical diagnosis applications.