A Survey on the New Generation of Deep Learning in Image Processing <s> 5) MASK R-CNN <s> Recently, high-dimensional shape data HDSD has been demonstrated to be informative in describing subcortical brain morphometry in several disorders. While HDSD may serve as a biomarker of disease, its high dimensionality may require careful treatment in its application to machine learning. Here, we compare several possible approaches for feature selection and pattern classification using HDSD. We explore the efficacy of three candidate feature selection FS methods: Guided Random Forest GRF, LASSO and no feature selection NFS. Each feature set was applied to three classifiers: Random Forest RF, Support Vector Machines SVM and Naive Bayes NB. Each model was cross-validated using two diagnostic contrasts: Alzheimer's Disease and mild cognitive impairment; each relative to matched controls. GRF and NFS outperformed LASSO as FS methods and were comparably competitive. NB underperformed relative to RF and SVM, which were comparable in performance. Our results advocate the NFS-RF approach for its speed, simplicity and interpretability. <s> BIB001 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> 5) MASK R-CNN <s> State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [1] and Fast R-CNN [2] have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features—using the recently popular terminology of neural networks with ’attention’ mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model [3] , our detection system has a frame rate of 5 fps ( including all steps ) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available. <s> BIB002 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> 5) MASK R-CNN <s> Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But pyramid representations have been avoided in recent object detectors that are based on deep convolutional networks, partially because they are slow to compute and memory intensive. In this paper, we exploit the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost. A top-down architecture with lateral connections is developed for building high-level semantic feature maps at all scales. This architecture, called a Feature Pyramid Network (FPN), shows significant improvement as a generic feature extractor in several applications. Using a basic Faster R-CNN system, our method achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles, surpassing all existing single-model entries including those from the COCO 2016 challenge winners. In addition, our method can run at 5 FPS on a GPU and thus is a practical and accurate solution to multi-scale object detection. Code will be made publicly available. <s> BIB003
Mask R-CNN is a simple, flexible, and general framework for object instance segmentation [55] . This framework consists of two stages, the first stage, called an RPN, proposes candidate object bounding boxes. The second stage, which extends Faster R-CNN BIB002 (see black flowchart in Fig.3 ) by adding a branch for predicting binary segmentation masks on each RoI, in parallel with the current branch for softmax classification and bounding box regression. Significantly, Faster R-CNN does not design for pixel-to-pixel alignment between network inputs and outputs. RoIAlign is a simple quantization-free layer for fixing the misalignment, which faithfully preserves exact spatial locations. Further, during the training phase, a multi-task loss on each sampled RoI can be expressed as follows, BIB001 where L cls and L box is classification loss and bounding-box loss, respectively. Moreover, the mask branch has a Km 2dimensional output for each RoI, which encodes K binary masks of resolution m × m, one for each of the K classes. To this, we apply a per-pixel sigmoid and define L mask as the average binary cross-entropy loss. Besides, for an RoI associated with ground-truth class k, L mask is only defined on the k-th mask. For the Mask R-CNN network architecture in Fig.3 , we can differentiate between the convolutional Backbone architecture used for feature extraction over an entire image and the network head for bounding-box recognition (softmax classification and regression) and binary mask prediction that is applied separately to each RoI. Specifically, one can using the nomenclature network-depth-features for the Backbone architecture, such as ResNet networks of depth 50 or 101 layers, and with an FPN BIB003 backbone extracts RoI features from different levels of the feature pyramid according to their scale. Therefore, Using a ResNet-50/ 101-FPN backbone for feature extraction captures excellent gains in both accuracy and speed. For the network head, which closely follows architectures presented in previous work to which adds a fully convolutional mask prediction branch; the head part on the ResNet-50/101-FPN that uses fewer filters is more efficient. Finally, RPN can be trained separately and does not share features of Mask R-CNN unless specified.