Medical image segmentation is vital to the area of medical imaging because it enables professionals to more accurately examine and understand the information offered by different imaging modalities. The technique of splitting a medical image into various segments or regions of interest is known as medical image segmentation. The segmented images that are produced can be used for many different things, including diagnosis, surgery planning, and therapy evaluation. In initial phase of research, major focus has been given to review existing deep-learning approaches, including researches like MultiResUNet, Attention U-Net, classical U-Net, and other variants. The attention feature vectors or maps dynamically add important weights to critical information, and most of these variants use these to increase accuracy, but the network parameter requirements are somewhat more stringent. They face certain problems such as overfitting, as their number of trainable parameters is very high, and so is their inference time. Therefore, the aim of this research is to reduce the network parameter requirements using depthwise separable convolutions, while maintaining performance over some medical image segmentation tasks such as skin lesion segmentation using attention system and residual connections.

翻译：医学图像分割在医学成像领域至关重要，因为它使专业人员能够更准确地检查和理解不同成像模态所提供的信息。将医学图像分割成不同区域或感兴趣区域的技术被称为医学图像分割。生成的分割图像可用于多种用途，包括诊断、手术规划和治疗评估。在研究初期阶段，主要关注点在于回顾现有的深度学习方法，包括MultiResUNet、Attention U-Net、经典U-Net及其他变体等研究。注意力特征向量或映射动态地为关键信息赋予重要权重，大多数这类变体利用这些机制来提高精度，但对网络参数的需求更为严格。它们面临过拟合等问题，因为其可训练参数数量非常大，推理时间也很长。因此，本研究的目的是利用深度可分离卷积减少网络参数需求，同时在部分医学图像分割任务（如使用注意力系统和残差连接的皮肤病变分割）中保持性能。