论文标题
Busu-net：用于医疗图像细分的合奏U-NET框架
BUSU-Net: An Ensemble U-Net Framework for Medical Image Segmentation
论文作者
论文摘要
近年来，卷积神经网络（CNN）彻底改变了医学图像分析。语义分割中最著名的CNN体系结构之一是U-NET，它在几种医疗图像分割应用中取得了很大的成功。同样，随着神经体系结构搜索（NAS）中自动广告的进步的兴起，在医学图像细分中提出了NAS之类的方法。在本文中，借助Laddernet，U-Net，Automl和NAS的灵感，我们提出了一个合奏深神经网络，其基础U-NET框架由双向卷积LSTMS和密集的连接组成，其中第一个（从左）（左）U-Net网络像第二个（左）更深（从左上）。我们表明，该合奏网络在几个评估指标中的最新网络都优于最新的网络，并且还评估了该合奏网络的轻量级版本，该版本也优于某些评估指标的最新最新网络。
In recent years, convolutional neural networks (CNNs) have revolutionized medical image analysis. One of the most well-known CNN architectures in semantic segmentation is the U-net, which has achieved much success in several medical image segmentation applications. Also more recently, with the rise of autoML ad advancements in neural architecture search (NAS), methods like NAS-Unet have been proposed for NAS in medical image segmentation. In this paper, with inspiration from LadderNet, U-Net, autoML and NAS, we propose an ensemble deep neural network with an underlying U-Net framework consisting of bi-directional convolutional LSTMs and dense connections, where the first (from left) U-Net-like network is deeper than the second (from left). We show that this ensemble network outperforms recent state-of-the-art networks in several evaluation metrics, and also evaluate a lightweight version of this ensemble network, which also outperforms recent state-of-the-art networks in some evaluation metrics.
