提出可自适应缩放的编码器，让模型在不同分辨率下保持一致性能。
Resolution Invariant Autoencoder
- 每层动态学习缩放因子，替代固定2倍下采样
- 输入输出分辨率变化时，潜空间分辨率保持一致
- 适用于超分辨、分类和生成任务，兼容性强
深度学习显著推动了医学图像分析，但图像分辨率差异仍是一个被忽视的挑战。现有方法多通过重采样解决，导致信息损失或计算效率低下。尽管特定任务有解决方案，但缺乏统一框架。本文提出一种分辨率不变的自编码器，通过学习变量缩放过程，在网络各层动态调整空间尺寸，取代传统固定2倍下采样。该设计确保无论输入输出分辨率如何变化，潜空间分辨率始终保持一致。模型支持在统一潜空间上执行多种下游任务，并在不确定性感知超分辨、分类和生成建模任务中表现优异，相比传统基线在跨分辨率场景下仅产生微小性能损失。
原文摘要 · Abstract (English)
Deep learning has significantly advanced medical imaging analysis, yet variations in image resolution remain an overlooked challenge. Most methods address this by resampling images, leading to either information loss or computational inefficiencies. While solutions exist for specific tasks, no unified approach has been proposed. We introduce a resolution-invariant autoencoder that adapts spatial resizing at each layer in the network via a learned variable resizing process, replacing fixed spatial down/upsampling at the traditional factor of 2. This ensures a consistent latent space resolution, regardless of input or output resolution. Our model enables various downstream tasks to be performed on an image latent whilst maintaining performance across different resolutions, overcoming the shortfalls of traditional methods. We demonstrate its effectiveness in uncertainty-aware super-resolution, classification, and generative modelling tasks and show how our method outperforms conventional baselines with minimal performance loss across resolutions.
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