HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation
HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation
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DOI：
10.1109/tmi.2018.2878669
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发表时间：
2019-05-01
影响因子：
10.6
通讯作者：
Ben Ayed, Ismail
中科院分区：
文献类型：
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作者：
Dolz, Jose;Gopinath, Karthik;Ben Ayed, Ismail
最近，密集连接在计算机视觉中引起了广泛的关注，因为它们在训练过程中促进了梯度流和隐式深度监督。特别是DenseNet，它以前馈方式将每一层连接到其他每一层，并在自然图像分类任务中表现出令人印象深刻的性能。我们提出了HyperDenseNet，这是一个三维全卷积神经网络，它将密集连接的定义扩展到多模态分割问题。每种成像模式都有一个路径，不仅在同一路径内的层对之间，而且在不同路径上的层对之间都有密集的连接。这与现有的多模态CNN方法形成对比，在现有的多模态CNN方法中，对几种模态进行建模完全依赖于单个联合层（或抽象层）进行融合，通常在网络的输入或输出处。因此，所提出的网络具有完全的自由来学习模态之间的更复杂的组合，在所有抽象级别之内和之间，这显着增加了学习表示。我们报告了对两个不同且极具竞争力的多模态脑组织分割挑战iSEG 2017和MRBrainS 2013的广泛评估，前者侧重于六个月的婴儿数据，后者侧重于成人图像。HyperDenseNet在许多最先进的细分网络上取得了显着的改进，在两个基准中都名列前茅。我们进一步提供了一个全面的实验分析的功能重用，这证实了超密集连接的重要性，在多模态表示学习。我们的代码是公开的。
Recently, dense connections have attracted substantial attention in computer vision because they facilitate gradient flow and implicit deep supervision during training. Particularly, DenseNet that connects each layer to every other layer in a feed-forward fashion and has shown impressive performances in natural image classification tasks. We propose HyperDenseNet, a 3-D fully convolutional neural network that extends the definition of dense connectivity to multi-modal segmentation problems. Each imaging modality has a path, and dense connections occur not only between the pairs of layers within the same path but also between those across different paths. This contrasts with the existing multi-modal CNN approaches, in which modeling several modalities relies entirely on a single joint layer (or level of abstraction) for fusion, typically either at the input or at the output of the network. Therefore, the proposed network has total freedom to learn more complex combinations between the modalities, within and in-between all the levels of abstraction, which increases significantly the learning representation. We report extensive evaluations over two different and highly competitive multi-modal brain tissue segmentation challenges, iSEG 2017 and MRBrainS 2013, with the former focusing on six month infant data and the latter on adult images. HyperDenseNet yielded significant improvements over many state-of-the-art segmentation networks, ranking at the top on both benchmarks. We further provide a comprehensive experimental analysis of features re-use, which confirms the importance of hyperdense connections in multi-modal representation learning. Our code is publicly available.