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

Our aim is to recognise the words being spoken by a talking face, given only the video but not the audio. Existing works in this area have focussed on trying to recognise a small number of utterances in controlled environments (e.g. digits and alphabets), partially due to the shortage of suitable datasets.

Lip reading in the wild

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.

Lip reading from visual channels remains a challenging topic considering the various speaking characteristics. In this paper, we address an efficient lip reading approach by investigating the unsupervised random forest manifold alignment (RFMA). The density random forest is employed to estimate affinity of patch trajectories in speaking facial videos. We propose novel criteria for node splitting to avoid the rank-deficiency in learning density forests. By virtue of the hierarchical structure of random forests, the trajectory affinities are measured efficiently, which are used to find embeddings of the speaking video clips by a graph-based algorithm. Lip reading is formulated as matching between manifolds of query and reference video clips. We employ the manifold alignment technique for matching, where the L∞-norm-based manifold-to-manifold distance is proposed to find the matching pairs. We apply this random forest manifold alignment technique to various video data sets captured by consumer cameras. The experiments demonstrate that lip reading can be performed effectively, and outperform state-of-the-arts.

/pdf/unsupervised-random-forest-manifold-alignment-for-lipreading-ip1lhdmbkb.pdf

Unsupervised Random Forest Manifold Alignment for Lipreading

Diffeomorphic registration is widely used in medical image processing with the invertible and one-to-one mapping between images. Recent progress has been made to diffeomorphic registration by utilizing a convolutional neural network for efficient and end-to-end inference of registration fields from an image pair. However, existing deep learning-based registration models neglect to employ attention mechanisms to handle the long-range cross-image relevance in embedding learning, limiting such approaches to identify the semantically meaningful correspondence of anatomical structures. In this paper, we propose a novel dual transformer network (DTN) for diffeomorphic registration, consisting of a learnable volumetric embedding module, a dual cross-image relevance learning module for feature enhancement, and a registration field inference module. The self-attention mechanisms of DTN explicitly model both the inter- and intra-image relevances in the embedding from both the separate and concatenated volumetric images, facilitating semantical correspondence of anatomical structures in diffeomorphic registration. Extensive quantitative and qualitative evaluations demonstrate that the DTN performs favorably against state-of-the-art methods.

Learning Dual Transformer Network for Diffeomorphic Registration

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

Objective: To establish an objective method for evaluating facial attractiveness from a set of orthodontic photographs. Materials and Methods: One hundred eight malocclusion patients randomly selected from six universities in China were randomly divided into nine groups, with each group containing an equal number of patients with Class I, II, and III malocclusions. Sixty-nine expert Chinese orthodontists ranked photographs of the patients (frontal, lateral, and frontal smiling photos) before and after orthodontic treatment from “most attractive” to “least attractive” in each group. A weighted mean ranking was then calculated for each patient, based on which a three-point scale was created. Procrustes superimposition was conducted on 101 landmarks identified on the photographs. A support vector regression (SVR) function was set up according to the coordinate values of identified landmarks of each photographic set and its corresponding grading. Its predictive ability was tested for each group in tu...

Evaluation of facial attractiveness for patients with malocclusion: A machine-learning technique employing Procrustes

The craniofacial reconstruction is employed as an initialization of the identification from skulls in forensics. In this paper, we present a two-level craniofacial reconstruction framework based on the local structural diversity of the skulls. On the low level, the holistic reconstruction is formulated as the superimposition of the selected tissue map on the novel skull. The crux is the accurate map registration, which is implemented as a warping guided by the 2D feature curve patterns. The curve pattern extraction under an energy minimization framework is proposed for the automatic feature labeling on the skull depth map. The feature configuration on the warped tissue map is expected to resemble that on the novel skull. In order to make the reconstructed faces personalized, on the high level, the local facial features are estimated from the skull measurements via a RBF model. The RBF model is learnt from a dataset of the skull and the face feature pairs extracted from the head volume data. The experiments demonstrate the facial outlooks can be reconstructed feasibly and efficiently.

The Craniofacial Reconstruction from the Local Structural Diversity of Skulls

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