Computational Analysis and Deep Learning for Medical Care. Группа авторов

Computational Analysis and Deep Learning for Medical Care - Группа авторов


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i.e., each layer has direct access to the gradients from the loss function and the original input image. Further, DenseNets needs small set of parameters as compared to the traditional CNN and reduces vanishing gradient problem. Figure 1.10 shows the architecture of DenseNet, and Table 1.10 shows various DenseNet architectures.

      1.2.10 MobileNets

      Mader [11] proposed V-Net for the detection of IVD. Bateson [12] propose a method which embeds domain-invariant prior knowledge and employ ENet to segment IVD. Other works which deserve special mentioning for the detection and segmentation of IVD from a 3D Spine MRI includes Zeng [13] uses CNN; Chang Liu [14] utilized 2.5D multi-scale FCN; Gao [15] presented a 2D CNN and DenseNet; Jose [17] presents a HD-UNet asym model; and Claudia Iriondo [16] uses VNet-based 3D connected component analysis algorithm.

      In this Chapter, we had discussed about the various CNN architectural models and its parameters. In the first phase, various architectures such as LeNet, AlexNet, VGGnet, GoogleNet, ResNet, ResNeXt, SENet, and DenseNet and MobileNet are studied. In the second phase, the application of CNN for the segmentation of IVD is presented. The comparison with state-of-the-art of segmentation approaches for spine T2W images are also presented. From the experimental results, it is clear that 2.5D multi-scale FCN outperforms all other models. As a future study, this work modify any currents models to get optimized results.

Type/Stride Filter shape Input size
Conv / s2 3 × 3 × 3 × 32 224 × 224 × 3
Conv dw / s1 3 × 3 × 32 dw 112 × 112 × 32
Conv / s1 1 × 1 × 32 × 64 112 × 112 × 32
Conv dw / s2 3 × 3 × 64 dw 112 × 112 × 64
Conv / s1 1 × 1 × 64 × 128 56 × 56 × 64
Conv dw / s1 3 × 3 × 128 dw 56 × 56 × 128
Conv / s1 1 × 1 × 128 × 128 56 × 56 × 128
Conv dw / s2 3 × 3 × 128 dw 56 × 56 × 128
Conv / s1 1 × 1 × 1 × 128 × 256 28 × 28 × 128
Conv dw / s1 3 × 3 × 256 dw 28 × 28 × 256
Conv / s1
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