Seeded iterative clustering for histology region identification

Eduard Chelebian, Francesco Ciompi, Carolina Wahlby

Keywords: clustering, segmentation, sparse annotations, pretrained embeddings, transfer learning, histology

TL;DR: We present a seeded iterative clustering method to classify histology regions, based on latent representations of different pretrained networks

Abstract: Annotations are necessary to develop computer vision algorithms for histopathology, but dense annotations at a high resolution are often time-consuming to make. Deep learning models for segmentation are a way to alleviate the process, but require large amounts of training data, training times and computing power. To address these issues, we present seeded iterative clustering to produce a coarse segmentation densely and at the whole slide level. The algorithm uses precomputed representations as the clustering space and a limited amount of sparse interactive annotations as seeds to iteratively classify image patches. We obtain a fast and effective way of generating dense annotations for whole slide images and a framework that allows the comparison of neural network latent representations in the context of transfer learning.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/seeded-iterative-clustering-for-histology/code)