论文标题
卷积复发的残余U-NET嵌入了注意机制和局灶性Tversky损失函数以癌检测
Convolutional Recurrent Residual U-Net Embedded with Attention Mechanism and Focal Tversky Loss Function for Cancerous Nuclei Detection
论文作者
论文摘要
自本十年初以来，CNN一直是计算机视觉任务领域非常成功的工具。CNN的发明是从神经科学的灵感来源的，它与我们的视觉系统具有许多解剖学相似性。受到人类视图系统的解剖学系统，佩戴现有的U-Net架构可以改善许多方法。由于人类视觉系统使用注意机制，我们已经使用了注意串联来代替正常征服。尽管CNN本质上纯粹是馈送的，但解剖学证据表明，我们的大脑包含复发的突触，并且它们通常超过了馈送馈送和自上而下的连接。此事实启发到userecurrent卷积连接在untrancorncurnent connections in untranconcollent voltolution blocksin u-net的位置。本文还解决了医疗图像分析领域中的类不平衡问题。在最新的损失功能的帮助下，阶级失调的纸毛索申科。经过培训的建筑群是通过一些培训数据端训练的，它优于U-NET的其他变体。
Since the beginning of this decade, CNN has been a very successful tool in the field of Computer Vision tasks.The invention of CNN was inspired from neuroscience and it shares a lot of anatomical similarities with our visual system.Inspired by the anatomyof humanvisual system, wearguethat the existing U-Net architecture can be improvedin many ways. As human visual system uses attention mechanism, we have used attention concatenation in place of normalconcatenation.Although, CNN is purely feed-forward in nature but anatomical evidences show that our brain contains recurrent synapses and they often outnumber feed-forward and top-down connections. Thisfact inspiresus to userecurrent convolution connectionsin place of normalconvolution blocksin U-Net.Thispaper also addressesthe class imbalance issuein the field of medical image analysis. The paperresolvestheproblem of class imbalanceswith the help of state-of-the-art loss functions.Weargue thatourproposed architecturecan be trained end to end with a few training data and it outperforms the other variantsof U-Net.
