Forming Local Intersections of Projections for Classifying and Searching Histopathology Images

TL;DR

This paper introduces FLIP and mFLIP, novel image descriptors based on local Radon transform projections, for classifying histopathology images, demonstrating promising results on public datasets and outperforming some existing deep learning features.

Contribution

The paper proposes a new local image descriptor, FLIP and its multi-resolution version mFLIP, for histopathology image representation and classification.

Findings

FLIP outperforms non-fine-tuned Inception-v3.

mFLIP outperforms fine-tuned Inception-v3.

Both descriptors show promising results on KIMIA datasets.

Abstract

In this paper, we propose a novel image descriptor called Forming Local Intersections of Projections (FLIP) and its multi-resolution version (mFLIP) for representing histopathology images. The descriptor is based on the Radon transform wherein we apply parallel projections in small local neighborhoods of gray-level images. Using equidistant projection directions in each window, we extract unique and invariant characteristics of the neighborhood by taking the intersection of adjacent projections. Thereafter, we construct a histogram for each image, which we call the FLIP histogram. Various resolutions provide different FLIP histograms which are then concatenated to form the mFLIP descriptor. Our experiments included training common networks from scratch and fine-tuning pre-trained networks to benchmark our proposed descriptor. Experiments are conducted on the publicly available dataset KIMIA Path24 and KIMIA Path960. For both of these datasets, FLIP and mFLIP descriptors show promising results in all experiments.Using KIMIA Path24 data, FLIP outperformed non-fine-tuned Inception-v3 and fine-tuned VGG16 and mFLIP outperformed fine-tuned Inception-v3 in feature extracting.