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Proceedings Paper

An adaptive online learning framework for practical breast cancer diagnosis
Author(s): Tianshu Chu; Jie Wang; Jiayu Chen
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Paper Abstract

This paper presents an adaptive online learning (OL) framework for supporting clinical breast cancer (BC) diagnosis. Unlike traditional data mining, which trains a particular model from a fixed set of medical data, our framework offers robust OL models that can be updated adaptively according to new data sequences and newly discovered features. As a result, our framework can naturally learn to perform BC diagnosis using experts’ opinions on sequential patient cases with cumulative clinical measurements. The framework integrates both supervised learning (SL) models for BC risk assessment and reinforcement learning (RL) models for decision-making of clinical measurements. In other words, online SL and RL interact with one another, and under a doctor’s supervision, push the patient’s diagnosis further. Furthermore, our framework can quickly update relevant model parameters based on current diagnosis information during the training process. Additionally, it can build flexible fitted models by integrating different model structures and plugging in the corresponding parameters during the prediction (or decision-making) process. Even when the feature space is extended, it can initialize the corresponding parameters and extend the existing model structure without loss of the cumulative knowledge. We evaluate the OL framework on real datasets from BCSC and WBC, and demonstrate that our SL models achieve accurate BC risk assessment from sequential data and incremental features. We also verify that the well-trained RL models provide promising measurement suggestions.

Paper Details

Date Published: 24 March 2016
PDF: 12 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 978524 (24 March 2016); doi: 10.1117/12.2216550
Show Author Affiliations
Tianshu Chu, Stanford Univ. (United States)
Jie Wang, Stanford Univ. (United States)
Jiayu Chen, GE Healthcare (United States)


Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato, Editor(s)

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