论文标题
使用Levit-Unet ++进行医学图像分割：关于GI道数据的案例研究
Medical Image Segmentation using LeViT-UNet++: A Case Study on GI Tract Data
论文作者
论文摘要
胃肠道癌被认为是胃肠道中器官的致命恶性状况。由于其死亡，迫切需要医疗图像分割技术来分割器官以减少治疗时间并增强治疗。传统的分割技术依赖于手工制作的功能，并且计算昂贵且效率低下。视觉变压器在许多图像分类和细分任务中都获得了巨大的知名度。为了从变形金刚的角度解决这个问题，我们引入了混合CNN-Transformer架构，以从图像中分割不同的器官。所提出的解决方案具有健壮，可扩展性和计算有效的效率，骰子和Jaccard系数分别为0.79和0.72。提出的解决方案还描述了基于深度学习的自动化的本质，以提高治疗的有效性
Gastro-Intestinal Tract cancer is considered a fatal malignant condition of the organs in the GI tract. Due to its fatality, there is an urgent need for medical image segmentation techniques to segment organs to reduce the treatment time and enhance the treatment. Traditional segmentation techniques rely upon handcrafted features and are computationally expensive and inefficient. Vision Transformers have gained immense popularity in many image classification and segmentation tasks. To address this problem from a transformers' perspective, we introduced a hybrid CNN-transformer architecture to segment the different organs from an image. The proposed solution is robust, scalable, and computationally efficient, with a Dice and Jaccard coefficient of 0.79 and 0.72, respectively. The proposed solution also depicts the essence of deep learning-based automation to improve the effectiveness of the treatment
