Explainable Deep Learning Models in Medical Image Analysis

By Amitojdeep Singh, Sourya Sengupta, Vasudevan Lakshminarayanan. Journal of Imaging, 2020

640 citations recorded by the source corpus.

Deep learning methods have been very effective for a variety of medical diagnostic tasks and have even outperformed human experts on some of those. However, the black-box nature of the algorithms has restricted their clinical use. Recent explainability studies aim to show the features that influence the decision of a model the most. The majority of literature reviews of this area have focused on taxonomy, ethics, and the need for explanations. A review of the current applications of explainable deep learning for different medical imaging tasks is presented here. The various approaches, challenges for clinical deployment, and the areas requiring further research are discussed here from a practical standpoint of a deep learning researcher designing a system for the clinical end-users.

Subfields: Artificial Intelligence, Health Informatics

View Paper: https://www.semanticscholar.org/paper/af6af40155860ac2fd36680e63686473803b5f52

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Citations in the corpus

Deep Learning for Medical Anomaly Detection – A Survey (2020, methodology): “Finally, as per [103] model interpretation techniques in the medical domain can be broadly categorised into attribution based and non-attribution based methods.”
Learning to Synthesize Programs as Interpretable and Generalizable Policies (2021, background): “[103] Amitojdeep Singh, Sourya Sengupta, and Vasudevan Lakshminarayanan.”
Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2021, methodology): “[14] 2020 √ Detection and predict-ion of disease using medical imaging.”
A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts (2021, background): “Application domain natural language processing [34] medicine [124,131] recommendation systems [101,148]”
Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond (2021, background): “Explanations for deep learningbased methods are more urged in this field than in other fields, to gain the trust of physicians, regulators as well as the patients [154].”
Doubly Right Object Recognition: A Why Prompt for Visual Rationales (2022, background): “Learning models that can explain their own decision is important for building trustworthy systems, especially in applications that require humanmachine interactions [2, 15, 37, 50].”
Deep Learning in Medical Image Analysis (2021, background): “Singh and Sengupta (2020) [22] from Canada present a review of the current applications of explainable deep learning for different medical imaging tasks.”
Transformers in Medical Imaging: A Survey (2022, background): “Although several explainable AIbased medical imaging systems have been developed to gain deeper insights into the working of CNNs models for clinical applications [394]–[396], however, the work is still in its infancy for ViT-based medical imaging applications.”