Combating Ambiguity for Hash-code Learning in Medical Instance Retrieval
This work addresses a critical challenge for radiologists in making evidence-based diagnoses by improving the accuracy of retrieving similar medical images from large databases, though it appears incremental as it builds on existing hash-code learning methods with specific enhancements.
The paper tackles the problem of manifestation ambiguity in medical instance retrieval, where similar visual features can correspond to different diseases or stages, by proposing Y-Net, a deep framework that unifies segmentation and classification losses to learn discriminative hash-codes, resulting in an average 9.27% improvement in retrieval performance over state-of-the-art methods on two datasets.
When encountering a dubious diagnostic case, medical instance retrieval can help radiologists make evidence-based diagnoses by finding images containing instances similar to a query case from a large image database. The similarity between the query case and retrieved similar cases is determined by visual features extracted from pathologically abnormal regions. However, the manifestation of these regions often lacks specificity, i.e., different diseases can have the same manifestation, and different manifestations may occur at different stages of the same disease. To combat the manifestation ambiguity in medical instance retrieval, we propose a novel deep framework called Y-Net, encoding images into compact hash-codes generated from convolutional features by feature aggregation. Y-Net can learn highly discriminative convolutional features by unifying the pixel-wise segmentation loss and classification loss. The segmentation loss allows exploring subtle spatial differences for good spatial-discriminability while the classification loss utilizes class-aware semantic information for good semantic-separability. As a result, Y-Net can enhance the visual features in pathologically abnormal regions and suppress the disturbing of the background during model training, which could effectively embed discriminative features into the hash-codes in the retrieval stage. Extensive experiments on two medical image datasets demonstrate that Y-Net can alleviate the ambiguity of pathologically abnormal regions and its retrieval performance outperforms the state-of-the-art method by an average of 9.27\% on the returned list of 10.