标题： Tree-NET：通过高效低级特征训练增强医学图像分割 显示英文标题

标题： Tree-NET: Enhancing Medical Image Segmentation Through Efficient Low-Level Feature Training

摘要： 本文介绍了Tree-NET，一种用于医学图像分割的新框架，该框架利用瓶颈特征监督来提高分割精度和计算效率。 虽然之前的研究已经采用了瓶颈特征监督，但其应用主要局限于训练阶段，在训练或评估期间没有提供计算上的优势。 据我们所知，本研究是第一个提出结合两个额外训练阶段的框架，用于分割模型，同时在输入和输出阶段利用瓶颈特征。 这种方法通过以可忽略的参数数量增加来减少输入和输出维度，显著提高了计算性能，而不会影响准确性。 Tree-NET具有由Encoder-Net和Decoder-Net组成的三层架构，它们是自动编码器，分别用于压缩输入和标签数据，以及Bridge-Net，一个监督瓶颈特征的分割框架。 通过专注于密集的压缩表示，Tree-NET提高了操作效率，并可以无缝集成到现有的分割模型中，而无需改变其内部结构或增加模型大小。 我们在两个关键的分割任务——皮肤病变和息肉分割——上对Tree-NET进行了评估，使用了多种主干模型，包括U-NET变体和Polyp-PVT。 实验结果表明，Tree-NET将FLOPs减少了4到13倍，并减少了内存使用，同时相比原始架构实现了相当或更好的准确性。 这些发现强调了Tree-NET在医学图像分割中的强大和高效解决方案的潜力。 显示英文摘要

摘要： This paper introduces Tree-NET, a novel framework for medical image segmentation that leverages bottleneck feature supervision to enhance both segmentation accuracy and computational efficiency. While previous studies have employed bottleneck feature supervision, their applications have largely been limited to the training phase, offering no computational benefits during training or evaluation. To the best of our knowledge, this study is the first to propose a framework that incorporates two additional training phases for segmentation models, utilizing bottleneck features at both input and output stages. This approach significantly improves computational performance by reducing input and output dimensions with a negligible addition to parameter count, without compromising accuracy. Tree-NET features a three-layer architecture comprising Encoder-Net and Decoder-Net, which are autoencoders designed to compress input and label data, respectively, and Bridge-Net, a segmentation framework that supervises the bottleneck features. By focusing on dense, compressed representations, Tree-NET enhances operational efficiency and can be seamlessly integrated into existing segmentation models without altering their internal structures or increasing model size. We evaluate Tree-NET on two critical segmentation tasks -- skin lesion and polyp segmentation -- using various backbone models, including U-NET variants and Polyp-PVT. Experimental results demonstrate that Tree-NET reduces FLOPs by a factor of 4 to 13 and decreases memory usage, while achieving comparable or superior accuracy compared to the original architectures. These findings underscore Tree-NET's potential as a robust and efficient solution for medical image segmentation.