Semi-Supervised Domain Adaptation with Latent Diffusion for Pathology Image Classification

TL;DR

This paper introduces a semi-supervised domain adaptation method using latent diffusion models to generate tissue-structure-preserving synthetic images, enhancing model generalization across different pathology cohorts.

Contribution

The work presents a novel diffusion-based augmentation framework conditioned on domain features, improving cross-cohort pathology image classification without distorting tissue structures.

Findings

Significant improvement in F1 scores on target cohort (from 0.611 to 0.706)

Synthetic images preserve tissue morphology and domain characteristics

Method enhances domain generalization in computational pathology.

Abstract

Deep learning models in computational pathology often fail to generalize across cohorts and institutions due to domain shift. Existing approaches either fail to leverage unlabeled data from the target domain or rely on image-to-image translation, which can distort tissue structures and compromise model accuracy. In this work, we propose a semi-supervised domain adaptation (SSDA) framework that utilizes a latent diffusion model trained on unlabeled data from both the source and target domains to generate morphology-preserving and target-aware synthetic images. By conditioning the diffusion model on foundation model features, cohort identity, and tissue preparation method, we preserve tissue structure in the source domain while introducing target-domain appearance characteristics. The target-aware synthetic images, combined with real, labeled images from the source cohort, are subsequently used to train a downstream classifier, which is then tested on the target cohort. The effectiveness of the proposed SSDA framework is demonstrated on the task of lung adenocarcinoma prognostication. The proposed augmentation yielded substantially better performance on the held-out test set from the target cohort, without degrading source-cohort performance. The approach improved the weighted F1 score on the target-cohort held-out test set from 0.611 to 0.706 and the macro F1 score from 0.641 to 0.716. Our results demonstrate that target-aware diffusion-based synthetic data augmentation provides a promising and effective approach for improving domain generalization in computational pathology.