Abstract
In this work, we introduce MedIL, a first-of-its-kind autoencoder built for encoding medical images with heterogeneous sizes and resolutions for image generation. Medical images are often large and heterogeneous, where fine details are of vital clinical importance. Image properties change drastically when considering acquisition equipment, patient demographics, and pathology, making realistic medical image generation challenging. Recent work in latent diffusion models (LDMs) has shown success in generating images resampled to a fixed-size. However, this is a narrow subset of the resolutions native to image acquisition, and resampling discards fine anatomical details. MedIL utilizes implicit neural representations to treat images as continuous signals, where encoding and decoding can be performed at arbitrary resolutions without prior resampling. We quantitatively and qualitatively show how MedIL compresses and preserves clinically-relevant features over large multi-site, multi-resolution datasets of both T1w brain MRIs and lung CTs. We further demonstrate how MedIL can influence the quality of images generated with a diffusion model, and discuss how MedIL can enhance generative models to resemble raw clinical acquisitions.
Abstract (translated)
在这项工作中，我们介绍了MedIL，这是一种首创的自动编码器，专门用于编码大小和分辨率各异的医学影像以生成图像。医学影像通常很大且异质化，在这些影像中，细微细节对于临床诊断至关重要。当考虑获取设备、患者人口统计学以及病理情况时，图像特性会发生剧烈变化，这使得真实的医学影像生成变得具有挑战性。最近在潜在扩散模型（LDMs）方面的工作已成功地用于生成调整到固定大小的图像，但这种方法仅适用于原始获取中有限的一组分辨率，并且重采样会丢失细微的解剖细节。MedIL利用隐式神经表示将图像视为连续信号，在这种情况下编码和解码可以在无需先进行重采样的条件下在任意分辨率下完成。 我们通过定量和定性的方法展示了MedIL如何对大型多中心、多分辨率的数据集中的T1加权脑MRI和肺部CT影像的临床相关特征进行压缩并保持。此外，我们还演示了MedIL是如何影响与扩散模型生成图像的质量，并讨论了MedIL可以如何增强生成模型以更接近原始临床获取的效果。
URL
https://arxiv.org/abs/2504.09322
