HistoKT: Cross Knowledge Transfer in Computational Pathology

TL;DR

This paper investigates the transferability of knowledge between histopathological datasets in computational pathology, proposing a data-centric approach and demonstrating effective knowledge sharing techniques like weight distillation.

Contribution

It introduces a standardized workflow for aggregating histopathological data and evaluates inter-domain knowledge transfer, highlighting the benefits of a two-stage learning framework and weight distillation.

Findings

Pretraining benefits multi-class datasets.

Two-stage learning improves small dataset performance.

Weight distillation outperforms natural image pretraining.

Abstract

The lack of well-annotated datasets in computational pathology (CPath) obstructs the application of deep learning techniques for classifying medical images. %Since pathologist time is expensive, dataset curation is intrinsically difficult. Many CPath workflows involve transferring learned knowledge between various image domains through transfer learning. Currently, most transfer learning research follows a model-centric approach, tuning network parameters to improve transfer results over few datasets. In this paper, we take a data-centric approach to the transfer learning problem and examine the existence of generalizable knowledge between histopathological datasets. First, we create a standardization workflow for aggregating existing histopathological data. We then measure inter-domain knowledge by training ResNet18 models across multiple histopathological datasets, and cross-transferring between them to determine the quantity and quality of innate shared knowledge. Additionally, we use weight distillation to share knowledge between models without additional training. We find that hard to learn, multi-class datasets benefit most from pretraining, and a two stage learning framework incorporating a large source domain such as ImageNet allows for better utilization of smaller datasets. Furthermore, we find that weight distillation enables models trained on purely histopathological features to outperform models using external natural image data.