论文标题
Histoseg：在数字组织学图像中，具有多结构分割的多层次功能的快速注意
HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images
论文作者
论文摘要
医学图像分割有助于计算机辅助诊断，手术和治疗。数字化组织载玻片图像用于分析和分段腺体，核和其他生物标志物，这些标志物进一步用于计算机辅助医疗应用中。为此，许多研究人员开发了不同的神经网络来对组织学图像进行分割，主要是这些网络基于编码器架构体系结构，并且还利用了复杂的注意模块或变压器。但是，这些网络在多个尺度上具有准确的边界检测捕获相关的本地和全局特征的准确性较差，因此，我们提出了一个编码器折叠网络，快速注意模块和多损耗函数（二进制交叉熵（BCE）损失，局灶性损失和二合一损失的组合）。我们在两个公开可用数据集上评估了我们提出的网络的概括能力，用于医疗图像分割Monuseg和Glas，并胜过最先进的网络，而Monuseg数据集则提高了1.99％，而GLAS数据集则提高了7.15％。实施代码可在此链接上获得：https：//bit.ly/histoseg
Medical image segmentation assists in computer-aided diagnosis, surgeries, and treatment. Digitize tissue slide images are used to analyze and segment glands, nuclei, and other biomarkers which are further used in computer-aided medical applications. To this end, many researchers developed different neural networks to perform segmentation on histological images, mostly these networks are based on encoder-decoder architecture and also utilize complex attention modules or transformers. However, these networks are less accurate to capture relevant local and global features with accurate boundary detection at multiple scales, therefore, we proposed an Encoder-Decoder Network, Quick Attention Module and a Multi Loss Function (combination of Binary Cross Entropy (BCE) Loss, Focal Loss & Dice Loss). We evaluate the generalization capability of our proposed network on two publicly available datasets for medical image segmentation MoNuSeg and GlaS and outperform the state-of-the-art networks with 1.99% improvement on the MoNuSeg dataset and 7.15% improvement on the GlaS dataset. Implementation Code is available at this link: https://bit.ly/HistoSeg
