Accurately segmenting different organs from medical images is a critical prerequisite for computer-assisted diagnosis and intervention planning. This study proposes a deep learning-based approach for segmenting various organs from CT and MRI scans and classifying diseases. Our study introduces a novel technique integrating momentum within residual blocks for enhanced training dynamics in medical image analysis. We applied our method in two distinct tasks: segmenting liver, lung, & colon data and classifying abdominal pelvic CT and MRI scans. The proposed approach has shown promising results, outperforming state-of-the-art methods on publicly available benchmarking datasets. For instance, in the lung segmentation dataset, our approach yielded significant enhancements over the TransNetR model, including a 5.72% increase in dice score, a 5.04% improvement in mean Intersection over Union (mIoU), an 8.02% improvement in recall, and a 4.42% improvement in precision. Hence, incorporating momentum led to state-of-the-art performance in both segmentation and classification tasks, representing a significant advancement in the field of medical imaging.

该研究解决了医学图像中不同器官准确分割的问题。论文提出了一种在残差块中集成动量的新技术，以增强医学图像分析中的训练动态。研究表明，该方法在肝脏、肺部和结肠分割及腹盆腔CT和MRI扫描分类任务中，在公共基准数据集上的表现超越了现有领先方法，标志着医学成像领域的重大进展。

一种新的基于动量的深度学习技术用于医学图像分类和分割