提出用MLP替代传统模型，更有效捕捉医学图像的细粒度长程依赖。
Toward Next-generation Medical Vision Backbones: Modeling Finer-grained Long-range Visual Dependency
- 用MLP建模高分辨率医学图像中的细粒度长程依赖
- 在多种任务上超越CNN与Transformer，提升性能
- 为下一代医学视觉骨干网络提供新范式
医学图像计算（MIC）涵盖像素级（如分割、配准）和图像级（如分类、回归）任务。有效分析需同时捕捉全局长程上下文与局部细微特征，因此需精细建模长程视觉依赖。相比受固有局部性限制的卷积神经网络（CNN），Transformer擅长长程建模，但自注意力计算开销大，通常无法处理高分辨率特征（如未下采样前的全图特征或补丁嵌入），难以建模医学图像中细微结构的长程依赖。与此同时，基于多层感知机（MLP）的视觉模型被证明在计算与内存效率上更具优势，但在MIC领域尚未广泛研究。本博士研究推进了深度学习在医学图像计算中的应用，首次将Transformer应用于像素级与图像级任务；随后聚焦于MLP，开创性地构建了基于MLP的视觉模型，以捕捉医学图像中细粒度的长程依赖。大量实验验证了长程依赖建模在MIC中的关键作用，并揭示一个重要发现：MLP可在保留丰富解剖/病理细节的高分辨率特征中，实现对细粒度长程依赖的有效建模。该发现确立了MLP优于传统Transformer与CNN的范式，在多种医学视觉任务中持续提升性能，为下一代医学视觉骨干网络铺平道路。
原文摘要 · Abstract (English)
Medical Image Computing (MIC) is a broad research topic covering both pixel-wise (e.g., segmentation, registration) and image-wise (e.g., classification, regression) vision tasks. Effective analysis demands models that capture both global long-range context and local subtle visual characteristics, necessitating fine-grained long-range visual dependency modeling. Compared to Convolutional Neural Networks (CNNs) that are limited by intrinsic locality, transformers excel at long-range modeling; however, due to the high computational loads of self-attention, transformers typically cannot process high-resolution features (e.g., full-scale image features before downsampling or patch embedding) and thus face difficulties in modeling fine-grained dependency among subtle medical image details. Concurrently, Multi-layer Perceptron (MLP)-based visual models are recognized as computation/memory-efficient alternatives in modeling long-range visual dependency but have yet to be widely investigated in the MIC community. This doctoral research advances deep learning-based MIC by investigating effective long-range visual dependency modeling. It first presents innovative use of transformers for both pixel- and image-wise medical vision tasks. The focus then shifts to MLPs, pioneeringly developing MLP-based visual models to capture fine-grained long-range visual dependency in medical images. Extensive experiments confirm the critical role of long-range dependency modeling in MIC and reveal a key finding: MLPs provide feasibility in modeling finer-grained long-range dependency among higher-resolution medical features containing enriched anatomical/pathological details. This finding establishes MLPs as a superior paradigm over transformers/CNNs, consistently enhancing performance across various medical vision tasks and paving the way for next-generation medical vision backbones.
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