InceptionMamba：面向显微医学图像分割的基于选择性状态空间模型的高效多阶段特征增强方法
InceptionMamba: Efficient Multi-Stage Feature Enhancement with Selective State Space Model for Microscopic Medical Image Segmentation
AI总结：
针对现有显微医学图像分割方法捕捉复杂结构难、依赖大数据且计算成本高的问题，提出InceptionMamba框架，通过多阶段特征增强与混合卷积-状态空间模型结构，在多个数据集上实现SOTA性能且计算成本降为原最优方法的1/5。
摘要：
精准的显微医学图像分割在多种癌细胞诊断与肿瘤识别中发挥着关键作用。在深度学习发展的推动下，卷积神经网络（CNNs）与基于transformer的模型已被广泛研究，用于扩大感受野并优化医学图像分割任务。但这类方法在背景杂乱、目标重叠等复杂场景下，往往难以捕捉复杂的细胞与组织结构。此外，它们依赖大规模数据集才能提升性能，且计算成本高昂，限制了实际应用。为解决这些问题，我们提出了一种用于分割任务的高效框架InceptionMamba，它可编码多阶段丰富特征，同时兼顾性能与计算效率。具体而言，我们利用语义线索同时捕捉低频与高频区域，丰富多阶段特征，以处理模糊的区域边界（如细胞边界）。这些增强后的特征被输入一个结合了Inception深度可分离卷积与Mamba模块的混合模型，在保持高效率的同时，捕捉感兴趣区域尺度与形状的固有变化。随后将这些增强特征与低分辨率特征融合，得到最终的分割掩码。我们的模型在两个具有挑战性的显微分割数据集（SegPC21和GlaS）以及两个皮肤病变分割数据集（ISIC2017和ISIC2018）上取得了当前最优性能，同时计算成本较此前的最佳性能方法降低了约5倍。
英文摘要：
Accurate microscopic medical image segmentation plays a crucial role in diagnosing various cancerous cells and identifying tumors. Driven by advancements in deep learning, convolutional neural networks (CNNs) and transformer-based models have been extensively studied to enhance receptive fields and improve medical image segmentation task. However, they often struggle to capture complex cellular and tissue structures in challenging scenarios such as background clutter and object overlap. Moreover, their reliance on the availability of large datasets for improved performance, along with the high computational cost, limit their practicality. To address these issues, we propose an efficient framework for the segmentation task, named InceptionMamba, which encodes multi-stage rich features and offers both performance and computational efficiency. Specifically, we exploit semantic cues to capture both low-frequency and high-frequency regions to enrich the multi-stage features to handle the blurred region boundaries (e.g., cell boundaries). These enriched features are input to a hybrid model that combines an Inception depth-wise convolution with a Mamba block, to maintain high efficiency and capture inherent variations in the scales and shapes of the regions of interest. These enriched features along with low-resolution features are fused to get the final segmentation mask. Our model achieves state-of-the-art performance on two challenging microscopic segmentation datasets (SegPC21 and GlaS) and two skin lesion segmentation datasets (ISIC2017 and ISIC2018), while reducing computational cost by about 5 times compared to the previous best performing method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
