Deep Barcodes for Fast Retrieval of Histopathology Scans
This work addresses efficient big data retrieval for medical imaging, but it is incremental as it builds on existing pre-trained networks and binary search methods.
The paper tackled the problem of fast retrieval of histopathology images by proposing deep barcodes, achieving a retrieval accuracy of 71.62%, which outperformed deep features (68.91%) and compressed deep features (68.53%).
We investigate the concept of deep barcodes and propose two methods to generate them in order to expedite the process of classification and retrieval of histopathology images. Since binary search is computationally less expensive, in terms of both speed and storage, deep barcodes could be useful when dealing with big data retrieval. Our experiments use the dataset Kimia Path24 to test three pre-trained networks for image retrieval. The dataset consists of 27,055 training images in 24 different classes with large variability, and 1,325 test images for testing. Apart from the high-speed and efficiency, results show a surprising retrieval accuracy of 71.62% for deep barcodes, as compared to 68.91% for deep features and 68.53% for compressed deep features.