In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

/pdf/review-of-deep-learning-concepts-cnn-architectures-hpbk62qzc2.pdf

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

In this paper, a new technique for modeling textured 3D faces is introduced. 3D faces can either be generated automatically from one or more photographs, or modeled directly through an intuitive user interface. Users are assisted in two key problems of computer aided face modeling. First, new face images or new 3D face models can be registered automatically by computing dense one-to-one correspondence to an internal face model. Second, the approach regulates the naturalness of modeled faces avoiding faces with an ''unlikely'' appearance Starting from an example set of 3D face models, we derive a morphable face model by transforming the shape and texture of the examples into a vector space representation. New faces and expressions can be modeled by forming linear combinations of the prototypes. Shape and texture constraints derived from the statistics of our example faces are used to guide manual modeling or automated matching algorithms We show 3D face reconstructions from single images and their applications for photo-realistic image manipulations. We also demonstrate face manipulations according to complex parameters such as gender, fullness of a face or its distinctiveness.

A morphable model for the synthesis of 3D faces

This paper presents a review of deep learning (DL)-based medical image registration methods. We summarized the latest developments and applications of DL-based registration methods in the medical field. These methods were classified into seven categories according to their methods, functions and popularity. A detailed review of each category was presented, highlighting important contributions and identifying specific challenges. A short assessment was presented following the detailed review of each category to summarize its achievements and future potential. We provided a comprehensive comparison among DL-based methods for lung and brain registration using benchmark datasets. Lastly, we analyzed the statistics of all the cited works from various aspects, revealing the popularity and future trend of DL-based medical image registration.

pdf/deep-learning-in-medical-image-registration-a-review-4llppi168w.pdf

Deep learning in medical image registration: a review.

The establishment of image correspondence through robust image registration is critical to many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitoring and is a very challenging problem. Since the beginning of the recent deep learning renaissance, the medical imaging research community has developed deep learning-based approaches and achieved the state-of-the-art in many applications, including image registration. The rapid adoption of deep learning for image registration applications over the past few years necessitates a comprehensive summary and outlook, which is the main scope of this survey. This requires placing a focus on the different research areas as well as highlighting challenges that practitioners face. This survey, therefore, outlines the evolution of deep learning-based medical image registration in the context of both research challenges and relevant innovations in the past few years. Further, this survey highlights future research directions to show how this field may be possibly moved forward to the next level.

Deep learning in medical image registration: a survey

The 2D-3D registration is a cornerstone to align the inter-treatment X-ray images with the available volumetric images. In this paper, we propose a CNN regression based non-rigid 2D-3D registration method. An iterative refinement scheme is introduced to update the reference volumetric image and the digitally-reconstructed-radiograph (DRR) for convergence to the target X-ray image. The CNN-based regressor represents the mapping between an image pair and the in-between deformation parameters. In particular, the short residual connections in the convolution blocks and long jump connections for the multi-scale feature map fusion facilitate the information propagation in training the regressor. The proposed method has been applied to 2D-3D registration of synthetic X-ray and clinically-captured CBCT images. Experimental results demonstrate the proposed method realizes an accurate and efficient 2D-3D registration of craniofacial images.

Non-rigid Craniofacial 2D-3D Registration Using CNN-Based Regression

In this paper, we propose an end-to-end coupled 3D tooth segmentation and dense correspondence network (c-SCN) for annotation of individual teeth and gingiva of clinically-obtained 3D dental models. The proposed model can be stacked on an existing graph convolutional network (GCN) for feature extraction from dental meshes. We devise a branch network for the instance-aware geodesic maps with respect to virtual tooth crown centroids for feature enhancement. The geodesic map encodes the spatial relationship of an individual tooth with the remaining dental model, and is concatenated with the GCN-based vertex-wise feature fields for simultaneous tooth segmentation and labeling. Furthermore, the label probability matrix from the multi-category classifier, indicating individual tooth regions and boundaries, is used to enhance the inference of dense correspondence. By utilizing the smooth semantic correspondence with the preservation of geometric topology, our approach addresses the attribute transfer-based landmark location. The qualitative and quantitative evaluations on the clinically-obtained dental models of orthodontic patients demonstrate that our approach achieves effective tooth annotation and dense correspondence, outperforming the compared state-of-the-art.

Automatic Tooth Segmentation and Dense Correspondence of 3D Dental Model

This paper presents an approach to automatic and accurate segmentation and identification of individual teeth from digital dental casts via deep graph convolutional neural networks. Instead of performing the teeth-gingiva and inter-tooth segmentation in two separate phases, the proposed method enables the simultaneous segmentation and identification of the gingiva and teeth. We perform the vertex-wise feature learning via the feature steered graph convolutional neural network (FeaStNet) [1] that dynamically updates the mapping between convolutional filters and local patches from digital dental casts. The proposed framework handles the tightly intertwined segmentation and labeling tasks with a novel constraint on crown shape distribution and concave contours to remove ambiguous labeling of neighboring teeth. We further enforce the smooth segmentation using the pairwise relationship in local patches to penalize rough and inaccurate region boundaries and regularize the vertex-wise labeling in the training process. The qualitative and quantitative evaluations on the digital dental casts obtained in the clinical orthodontics demonstrate that the proposed method achieves efficient and accurate tooth segmentation and produces performance improvements to the state-of-the-art.

Tooth Segmentation and Labeling from Digital Dental Casts

Depth images are widely used in 3D head pose estimation and face reconstruction. The device-specific noise and the lack of textual constraints pose a major problem for estimating a nonrigid deformable face from a single noisy depth image. In this article, we present a deep neural network-based framework to infer a 3D face consistent with a single depth image captured by a consumer depth camera Kinect. Confronted with a lack of annotated depth images with facial parameters, we utilize the bidirectional CycleGAN-based generator for denoising and noisy image simulation, which helps to generalize the model learned from synthetic depth images to real noisy ones. We generate the code regressors in the source (synthetic) and the target (noisy) depth image domains and present a fusion scheme in the parametric space for 3D face inference. The proposed multi-level shape consistency constraint, concerning the embedded features, depth maps, and 3D surfaces, couples the code regressor and the domain adaptation, avoiding shape distortions in the CycleGAN-based generators. Experiments demonstrate that the proposed method is effective in depth-based 3D head pose estimation and expressive face reconstruction compared with the state-of-the-art.

Depth-Based 3D Face Reconstruction and Pose Estimation Using Shape-Preserving Domain Adaptation

The reconstruction of 3D face shapes and expressions from a single depth image obtained by a consumer depth camera is a challenging issue considering device-specific noise, the data missing, and the lack of textual constraints In order to relieve the computationally-intensive nonlinear optimization of traditional template-fitting-based methods, we aim to build an end-to-end regression framework between a depth image and a 3D face encoded by the identity, the expression, and the pose parameters Concerning the lack of paired depth images and 3D faces, we utilize the unsupervised CycleGAN-based network to adapt the regression model learned from the synthetic data to the real-captured noisy depth images Instead of separate depth image denoising and 3D face inference, we present a task-specific coupled loss for end-to-end 3D face estimation We propose a three-tier constraint for the shape consistency in the joint embedding, the depth image, and the surface space to avoid shape distortions in the unsupervised domain adaptation network We report promising qualitative results for the task of the face denoising and 3D face reconstruction from a single depth image

Face Denoising and 3D Reconstruction from A Single Depth Image

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