Challenges of Deep Learning in Medical Image Analysis-Improving Explainability and Trust

Deep learning has revolutionized the detection of diseases and is helping the healthcare sector break barriers in terms of accuracy and robustness to achieve efficient and robust computer-aided diagnostic systems. The application of deep learning techniques empowers automated AI-based utilities requ...

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Bibliographic Details
Published in:IEEE transactions on technology and society 2023-03, Vol.4 (1), p.68-75
Main Authors: Dhar, Tribikram, Dey, Nilanjan, Borra, Surekha, Sherratt, R. Simon
Format: Article
Language:English
Subjects:
Adversarial attacks
Adversarial machine learning
Artificial intelligence
Artificial neural networks
Attenuation
Autonomy
Biomedical imaging
Computer aided diagnosis
computer-aided diagnostic systems
convolutional neural network
Convolutional neural networks
Data augmentation
Deep learning
Diagnostic systems
Fractures
Health care
Hemorrhage
Human performance
Image analysis
Machine learning
Medical diagnostic imaging
medical image analysis
Medical imaging
Medical services
Task analysis
Training
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Summary:Deep learning has revolutionized the detection of diseases and is helping the healthcare sector break barriers in terms of accuracy and robustness to achieve efficient and robust computer-aided diagnostic systems. The application of deep learning techniques empowers automated AI-based utilities requiring minimal human supervision to perform any task related to medical diagnosis of fractures, tumors, and internal hemorrhage; preoperative planning; intra-operative guidance, etc. However, deep learning faces some major threats to the flourishing healthcare domain. This paper traverses the major challenges that the deep learning community of researchers and engineers faces, particularly in medical image diagnosis, like the unavailability of balanced annotated medical image data, adversarial attacks faced by deep neural networks and architectures due to noisy medical image data, a lack of trustability among users and patients, and ethical and privacy issues related to medical data. This study explores the possibilities of AI autonomy in healthcare by overcoming the concerns about trust that society has in autonomous intelligent systems.
ISSN:2637-6415
2637-6415
DOI:10.1109/TTS.2023.3234203