Learning to Hash for Large-Scale Medical Image Retrieval

Hashing is a popular approach for performing computationally efficient approximate nearest neighbor search by means of encoding data items into sequence of bits, such that the nearest neighbor search in the coding space is efficient and accurate. This thesis explores aspects of code-consistent training of hashing forests and deep learning models for end-to-end learning of hash codes and demonstrates that such hashing models can be leveraged to perform efficient and accurate large-scale content-based medical image retrieval.