Gram Barcodes for Histopathology Tissue Texture Retrieval
This addresses the need for efficient retrieval systems in digital pathology to help pathologists access similar diagnosed cases, though it is incremental as it builds on existing neural network methods.
The paper tackled the problem of histopathology image retrieval by proposing Gram barcodes as compact features based on high-order statistics from convolutional neural networks, achieving highly competitive results on three public datasets.
Recent advances in digital pathology have led to the need for Histopathology Image Retrieval (HIR) systems that search through databases of biopsy images to find similar cases to a given query image. These HIR systems allow pathologists to effortlessly and efficiently access thousands of previously diagnosed cases in order to exploit the knowledge in the corresponding pathology reports. Since HIR systems may have to deal with millions of gigapixel images, the extraction of compact and expressive image features must be available to allow for efficient and accurate retrieval. In this paper, we propose the application of Gram barcodes as image features for HIR systems. Unlike most feature generation schemes, Gram barcodes are based on high-order statistics that describe tissue texture by summarizing the correlations between different feature maps in layers of convolutional neural networks. We run HIR experiments on three public datasets using a pre-trained VGG19 network for Gram barcode generation and showcase highly competitive results.