Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studies

Abstract

The sudden outbreak and uncontrolled spread of COVID-19 disease is one of the most important global problems today. In a short period of time, it has led to the development of many deep neural network models for COVID-19 detection with modules for explainability. In this work, we carry out a systematic analysis of various aspects of proposed models. Our analysis revealed numerous mistakes made at different stages of data acquisition, model development, and explanation construction. In this work, we overview the approaches proposed in the surveyed Machine Learning articles and indicate typical errors emerging from the lack of deep understanding of the radiography domain. We present the perspective of both: experts in the field - radiologists and deep learning engineers dealing with model explanations. The final result is a proposed checklist with the minimum conditions to be met by a reliable COVID-19 diagnostic model.

References in corpus (5)

• CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
• Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models
• What do we need to build explainable AI systems for the medical domain?
• Learning Hierarchical Attention for Weakly-supervised Chest X-Ray Abnormality Localization and Diagnosis
• Evaluation of Contemporary Convolutional Neural Network Architectures for Detecting COVID-19 from Chest Radiographs

Cited by in corpus (5)

• Adversarial attacks and defenses in explainable artificial intelligence: A survey
• Towards Evaluating Explanations of Vision Transformers for Medical Imaging
• DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems
• LIMEcraft: Handcrafted superpixel selection and inspection for Visual eXplanations
• Requirement analysis for an artificial intelligence model for the diagnosis of the COVID-19 from chest X-ray data