A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis (1910.02923v2)

Abstract: 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.

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

The paper "A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis" by Budd et al. offers a comprehensive overview of how human interaction can enhance deep learning processes in medical image analysis. This exploration focuses on incorporating human input in various stages of deep learning applications to leverage the strengths of human judgment, particularly in the safety-critical field of medical diagnostics.

Central to this survey is the notion that the integration of humans within deep learning paradigms—referred to as Human-in-the-Loop (HITL) computing—could address many of the unique challenges posed by medical image analysis. The paper articulates four essential areas that hold potential for improving clinical practice through deep learning.

1. Active Learning (AL): The paper highlights active learning as a method for selecting optimal data subsets for annotation, which can significantly enhance model performance with limited labeled data. This is particularly relevant in the medical domain, where annotated datasets are expensive and scarce. Active learning strategies are posited to efficiently utilize expert time and resources by focusing efforts on the most informative samples.
2. Interaction with Model Outputs: The interaction between humans and model outputs is discussed as a bidirectional feedback loop where iterative human guidance steers model predictions towards clinically useful outputs. This encompasses model interpretation, refining predictions, and adapting results to align better with expert evaluations, ultimately enhancing trust and acceptance in clinical settings.
3. Practical Considerations: The paper explores the practicalities of implementing deep learning applications in clinical environments. It examines key considerations for full-scale deployment, such as the system's scalability, ease of use, and integration into existing workflows. Addressing these practical aspects is critical for realizing the potential of AI-driven tools in day-to-day clinical operations.
4. Future Prospective and Unanswered Questions: Budd et al. identify several knowledge gaps and suggest future research directions that could strengthen the confluence of active learning and HITL methodologies. The authors theorize that bridging these gaps could culminate in end-to-end systems capable of transforming clinical practice through potent AI applications.

The authors argue that while numerous challenges remain, the field of HITL computing is poised to significantly impact medical image analysis by merging active learning with deep learning frameworks. Methodological advancements in these areas promise more accurate and robust systems that enhance routine clinical tasks, thereby optimizing expert labor and potentially improving patient outcomes.

This paper serves as a crucial reference point for ongoing research efforts aimed at refining human-machine collaboration in healthcare. By rigorously defining pivotal areas of exploration and improvement, Budd et al. underscore the significance of maintaining a human-centric approach to AI deployment in medicine. Future developments are anticipated to yield more seamless and efficient integration of deep learning technologies, potentially redefining clinical diagnostics and therapeutic interventions.