FISHing in Uncertainty: Synthetic Contrastive Learning for Genetic Aberration Detection

TL;DR

This paper presents a synthetic contrastive learning approach for FISH image classification that reduces manual annotation needs, improves generalization, and provides better uncertainty calibration in genetic aberration detection.

Contribution

It introduces a novel synthetic data-based training method with contrastive and classification objectives for FISH image analysis, enhancing accuracy and uncertainty quantification.

Findings

Achieved 96.7% classification accuracy on real FISH images.

Demonstrated superior uncertainty calibration and generalization.

Reduced manual annotation and improved diagnostic workflow.

Abstract

Detecting genetic aberrations is crucial in cancer diagnosis, typically through fluorescence in situ hybridization (FISH). However, existing FISH image classification methods face challenges due to signal variability, the need for costly manual annotations and fail to adequately address the intrinsic uncertainty. We introduce a novel approach that leverages synthetic images to eliminate the requirement for manual annotations and utilizes a joint contrastive and classification objective for training to account for inter-class variation effectively. We demonstrate the superior generalization capabilities and uncertainty calibration of our method, which is trained on synthetic data, by testing it on a manually annotated dataset of real-world FISH images. Our model offers superior calibration in terms of classification accuracy and uncertainty quantification with a classification accuracy of 96.7% among the 50% most certain cases. The presented end-to-end method reduces the demands on personnel and time and improves the diagnostic workflow due to its accuracy and adaptability. All code and data is publicly accessible at: https://github.com/SimonBon/FISHing