L-CNN: Exploiting labeling latency in a CNN learning framework
Muhammad Jamal Afridi, Arun A. Ross, Erik M. Shapiro · 2016
A supervised learning system requires labeled data during the training phase. Obtaining labels can be an expensive process, especially in medical imaging applications where a qualified expert may be needed to carefully analyze images and annotate them. This constrains the amount of labeled data available. This study explores the possibility of incorporating labeling behavior (viz., labeling latency) in a supervised convolutional neural network (CNN) framework in order to improve its performance in the presence of limited labeled data. The problem of “spot” detection in MRI scans is considered in this work. In this two-class problem, (a) labeling behavior is available only during the training phase unlike traditional features that are available both during training and testing; and (b) the labeling behavior is associated with only one class (the positive samples) unlike other side information that is available for all classes. To address these issues, a new CNN architecture referred to as L-CNN is designed. The proposed method utilizes the labeling behavior of the expert to cluster the labeled data into multiple categories; a source CNN is then trained to distinguish between these categories. Next, a transfer learning paradigm is used where a target CNN is initialized using this source CNN and its weights updated with the limited labeled data that is available. Experimental results on an existing MRI database show that the proposed L-CNN performs better than a conventional CNN and, further, significantly outperforms the previous state-of-the-art, thereby establishing a new baseline for “spot” detection in MRI.