MedVAE: Efficient Automated Interpretation of Medical Images with Large Scale Generalizable Autoencoders The model was presented in the paper MedVAE: Efficient Automated Interpretation of Medical Images with Large Scale Generalizable Autoencoders. 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 t…
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