A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis

By Samuel Budd, Emma Claire Robinson, Bernhard Kainz. Medical Image Anal., 2019

617 citations recorded by the source corpus.

Fully automatic deep learning has become the state-of-the-art technique for many tasks including image acquisition, analysis and interpretation, and for the extraction of clinically useful information for computer-aided detection, diagnosis, treatment planning, intervention and therapy. However, the unique challenges posed by medical image analysis suggest that retaining a human end-user in any deep learning enabled system will be beneficial. In this review we investigate the role that humans might play in the development and deployment of deep learning enabled diagnostic applications and focus on techniques that will retain a significant input from a human end user. Human-in-the-Loop computing is an area that we see as increasingly important in future research due to the safety-critical nature of working in the medical domain. We evaluate four key areas that we consider vital for deep learning in the clinical practice: (1) Active Learning to choose the best data to annotate for optimal model performance; (2) Interaction with model outputs - using iterative feedback to steer models to optima for a given prediction and offering meaningful ways to interpret and respond to predictions; (3) Practical considerations - developing full scale applications and the key considerations that need to be made before deployment; (4) Future Prospective and Unanswered Questions - knowledge gaps and related research fields that will benefit human-in-the-loop computing as they evolve. We offer our opinions on the most promising directions of research and how various aspects of each area might be unified towards common goals.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/755774c14c6b344596ae5025e00e678c08136b4c

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In Artificial Intelligence

Citations in the corpus

A Survey of Human-in-the-loop for Machine Learning (2021, background): “Before our work, there were also quiet a few excellent works reviewing human-in-the-loop [26, 27, 28, 29, 30, 31], but we are the first review of human-in-the-loop for machine learning.”
Challenges in Deploying Machine Learning: A Survey of Case Studies (2020, background): “[30] show that interface design directly impacts the quality of applications built to collect annotations for unlabeled data.”
Human-in-the-loop machine learning: a state of the art (2022, background): “An extensive survey in medical image analysis, which serves as the basis for clinical decision making, is performed in Budd et al. (2021).”
A Survey of Deep Active Learning (2020, methodology): “[21] conducted a comprehensive review of DAL-related methods in the field of medical image analysis.”