InceptionMamba: Efficient Multi-Stage Feature Enhancement with Selective State Space Model for Microscopic Medical Image Segmentation

📄 arXiv: 2506.12208v1 📥 PDF

作者: Daniya Najiha Abdul Kareem, Abdul Hannan, Mubashir Noman, Jean Lahoud, Mustansar Fiaz, Hisham Cholakkal

分类: cs.CV

发布日期: 2025-06-13

💡 一句话要点

提出InceptionMamba以解决显微医学图像分割效率问题

🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture)

关键词: 显微医学图像分割 深度学习 卷积神经网络 特征增强 计算效率 癌症诊断 Mamba块

📋 核心要点

  1. 现有的医学图像分割方法在处理复杂细胞和组织结构时表现不佳，尤其是在背景杂乱和物体重叠的情况下。
  2. InceptionMamba框架通过编码多阶段丰富特征，结合语义线索来捕捉低频和高频区域，从而提高分割性能和计算效率。
  3. 该模型在SegPC21、GlaS、ISIC2017和ISIC2018等数据集上取得了最先进的性能，并显著降低了计算成本。

📝 摘要（中文）

准确的显微医学图像分割在癌细胞诊断和肿瘤识别中至关重要。尽管深度学习的进步使得卷积神经网络和基于变换器的模型在医学图像分割任务中得到了广泛研究，但它们在复杂的细胞和组织结构捕捉方面仍然面临挑战。为了解决这些问题，本文提出了一种名为InceptionMamba的高效框架，能够编码多阶段丰富特征，提供性能和计算效率。该模型在两个显微分割数据集和两个皮肤病变分割数据集上实现了最先进的性能，同时将计算成本降低了约5倍。

🔬 方法详解

问题定义：本文旨在解决显微医学图像分割中存在的效率低下和复杂结构捕捉不足的问题。现有方法在处理背景杂乱和细胞重叠时效果不佳，且对大数据集的依赖限制了其实用性。

核心思路：InceptionMamba框架通过多阶段特征增强，结合低频和高频区域的语义信息，来改善模糊区域边界的捕捉能力。该设计旨在提高分割精度，同时降低计算成本。

技术框架：该框架主要包括特征编码、Inception深度卷积与Mamba块的结合、特征融合等模块。通过这些模块，模型能够有效提取和融合多层次特征，最终生成分割掩膜。

关键创新：最重要的创新在于引入了多阶段特征增强机制和Mamba块的结合，显著提高了模型在不同尺度和形状区域的捕捉能力，与传统方法相比，具有更高的效率和准确性。

关键设计：模型采用了深度卷积网络结构，结合了特定的损失函数以优化分割效果。参数设置经过精心调整，以确保在不同数据集上的最佳性能。特征融合策略也经过优化，以提高最终分割结果的质量。

📊 实验亮点

InceptionMamba在SegPC21和GlaS等显微分割数据集上实现了最先进的性能，并在ISIC2017和ISIC2018皮肤病变分割数据集上也表现优异。与之前最佳方法相比，计算成本降低了约5倍，显示出显著的效率提升。

🎯 应用场景

该研究在医学图像分析领域具有广泛的应用潜力，尤其是在癌症诊断和治疗监测中。通过提高显微医学图像的分割精度，InceptionMamba能够帮助医生更准确地识别肿瘤和病变区域，从而提升临床决策的质量。未来，该方法还可扩展到其他医学成像领域，推动相关技术的发展。

📄 摘要（原文）

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.