Deep Multimodal Guidance for Medical Image Classification

TL;DR

This paper introduces a deep learning guidance approach that leverages information from superior imaging modalities to enhance the diagnostic performance of inferior modalities in medical image classification tasks.

Contribution

It proposes a lightweight guidance model that uses latent representations from superior modalities to improve inferior modality-based diagnosis without needing the superior modality during inference.

Findings

Boosts diagnostic accuracy of inferior modalities

Outperforms models trained on superior modalities in some cases

Achieves comparable results to multi-modality models

Abstract

Medical imaging is a cornerstone of therapy and diagnosis in modern medicine. However, the choice of imaging modality for a particular theranostic task typically involves trade-offs between the feasibility of using a particular modality (e.g., short wait times, low cost, fast acquisition, reduced radiation/invasiveness) and the expected performance on a clinical task (e.g., diagnostic accuracy, efficacy of treatment planning and guidance). In this work, we aim to apply the knowledge learned from the less feasible but better-performing (superior) modality to guide the utilization of the more-feasible yet under-performing (inferior) modality and steer it towards improved performance. We focus on the application of deep learning for image-based diagnosis. We develop a light-weight guidance model that leverages the latent representation learned from the superior modality, when training a model that consumes only the inferior modality. We examine the advantages of our method in the context of two clinical applications: multi-task skin lesion classification from clinical and dermoscopic images and brain tumor classification from multi-sequence magnetic resonance imaging (MRI) and histopathology images. For both these scenarios we show a boost in diagnostic performance of the inferior modality without requiring the superior modality. Furthermore, in the case of brain tumor classification, our method outperforms the model trained on the superior modality while producing comparable results to the model that uses both modalities during inference.