MedVAE: Efficient Automated Interpretation of Medical Images with Large-Scale Generalizable Autoencoders

Maya Varma, Ashwin Kumar, Rogier Van der Sluijs, Sophie Ostmeier, Louis Blankemeier, Pierre Joseph Marcel Chambon, Christian Bluethgen, Jip Prince, Curtis Langlotz, Akshay S Chaudhari

Keywords: computer-aided detection and diagnosis, variational autoencoders, efficiency

TL;DR: We introduce MedVAE, a family of large-scale medical image autoencoders. MedVAE is capable of downsizing medical images in order to improve downstream computational efficiency while preserving clinically-relevant features.

Abstract:

Medical images are acquired at high resolutions with large fields of view in order to capture fine-grained features necessary for clinical decision-making. Consequently, training deep learning models on medical images can incur large computational costs. In this work, we address the challenge of downsizing medical images in order to improve downstream computational efficiency while preserving clinically-relevant features. We introduce MedVAE, a family of six large-scale 2D and 3D autoencoders capable of encoding medical images as downsized latent representations and decoding latent representations back to high-resolution images. We train MedVAE autoencoders using a novel two-stage training approach with 1,052,730 medical images. Across diverse tasks obtained from 20 medical image datasets, we demonstrate that (1) utilizing MedVAE latent representations in place of high-resolution images when training downstream models can lead to efficiency benefits (up to 70x improvement in throughput) while simultaneously preserving clinically-relevant features and (2) MedVAE can decode latent representations back to high-resolution images with high fidelity. Our work demonstrates that large-scale, generalizable autoencoders can help address critical efficiency challenges in the medical domain.

Code: https://github.com/StanfordMIMI/MedVAE

Primary Subject Area: Unsupervised Learning and Representation Learning

Secondary Subject Area: Detection and Diagnosis

Paper Type: Methodological Development

Registration Requirement: Yes

Reproducibility: https://github.com/StanfordMIMI/MedVAE

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Submission Number: 153