Prostate cancer (PCa) is a malignancy originating from epithelial cells within the prostate gland. The gold standard for diagnosing PCa is typically based on the Gleason score. However, the inherent variability in biopsy sampling and its potential discordance with radical prostatectomy outcomes can result in the misclassification of the International Society of Urological Pathology (ISUP) Gleason Group (GG). Furthermore, the employment of prostate-specific antigen (PSA) for screening, guiding biopsy decisions, and the reduction in PSA biopsy thresholds has led to a notable increase in unwarranted biopsies among patients with PCa. Consequently, developing an efficient and accurate method to predict ISUP GG is imperative. Currently, most studies focus on ISUP GG binary classification for specific GG. In this study, we leverage multiparametric Magnetic Resonance Imaging (mpMRI) images, encompassing T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC), to introduce a post-fusion based cross-attention model named PFCA-Net. The model is designed to predict ISUP GG categories, specifically GG 0/1, GG 2, GG 3, and GG 4/5. We validate our approach using three distinct deep learning classification models and a mpMRI pre-fusion classification model. Our proposed approach demonstrates exceptional performance, attaining an accuracy (ACC) of 0.9692 and an area under the curve (AUC) of 0.9986, over a dataset comprising 107 PCa patients and encompassing a total of 927 MRI images. This study emphasizes the practical value of mpMRI in distinguishing various ISUP GG categories and expediting the evaluation of PCa GG by clinicians.

In this paper, we tackle the continuous gesture recognition problem with a two streams Recurrent Neural Networks (2S-RNN) for the RGB-D data input. In our framework, the spotting-recognition strategy is used, that means the continuous gestures are first segmented into separated gestures, and then each isolated gesture is recognized by using the 2S-RNN. Concretely, the gesture segmentation is based on the accurate hand positions provided by the hand detector trained from Faster R-CNN. While in the recognition module, 2S-RNN is designed to efficiently fuse multi-modal features, i.e. the RGB and depth channels. The experimental results on both the validation and test sets of the Continuous Gesture Dataset (ConGD) have shown promising performance of the proposed framework. We ranked 1st in the ChaLearn LAP Large-scale Continuous Gesture Recognition Challenge with the mean Jaccard Index of 0.286915.

Deaf person has a large social community around the world. The smooth communication is very difficult for these hard of hearings. Automatic Sign Language Recognition (SLR) can build the bridge between the deaf and the hearings and turn the seamless interaction into reality. This paper presents a visualized communication tool for the hard of hearings, i.e. a large vocabulary sign language recognition system based on the RGB-D data input. A novel Grassmann Covariance Matrix (GCM) representation is used to encode a long-term dynamics of a sign sequence and the discriminative kernel SVM is adopted for the sign classification. For continuous sign language recognition, a probability inference method is used to determine the spotting from the labels of sequential frames. Some basic evaluation and comparison of our recognition algorithms are conducted in our collected datasets. This demo will show the recognition of both isolated sign words and the continuous sign language sentences.

In this paper, we introduce metric learning into Sign Language Recognition(SLR) for the first time and propose a signer adaption framework to address signer-independent SLR. For adapting the general model to the new signer, both clustering and manifold constraints are considered in the adaptive distance metric optimization. The contribution of our work mainly lies in three-folds. Firstly, a Weakly Supervised Metric Learning(WSML) framework is proposed, which combines the clustering and manifold constraints simultaneously. Secondly, the general framework is applied to signer adaptation and achieves good performance. Thirdly, a fragment based feature is designed for sign language representation and the effectiveness is verified in large vocabulary datasets. Our proposed WSML framework can be decomposed into two key steps. The first one is to learn a generic metric from the given labeled data. Then the second step is to realize the distance metric adaptation by considering the clustering and manifold constraints with the unlabeled data. To learn a generic distance metric, the labeled data are used under clustering assumption with classical large margin hinge loss. Specifically, the distances between data points within the same cluster(with same label) should be minimized and the distances between data points from different clusters(with different labels) should be maximized. Here we define the index set with same labels as Sg = {(i, j)|yi = y j,xi,x j ∈ Xl} and the index triplet Bg = {(i, j,k)|yi = y j,yi 6= yk,xi,x j,xk ∈ Xl}. The objective function is

In this paper, a sparse coding based framework is proposed for sign language recognition (SLR), especially for the signer-independent case. To deal with the inter-signer variation, a dictionary capturing the common features among different signers is learnt by considering the semantic constraint. Thus for a given sign from an unknown signer, the sparse representation, which maintains more information of this specific sign class while neglecting the identity information as much as possible, can be generated. In our implementation, each sign is partitioned into a fixed number of fragments and the features fusing hand shape and moving trajectory are extracted from the fragments. The dictionary learnt from the training fragments can be taken as the basic subunits of signs and each fragment of sign video can be coded by these basis vectors. Finally, the recognition result is achieved through SVM with the concatenated sparse coding features of the fragments. The experiments and comparisons show that our method is more effective for the signer-independent recognition problem than other baseline methods. At the same time, it also performs well for the signer-dependent case.

In this paper, an error resilient coding structure is proposed. Firstly an extended dual frame motion compensation (DFMC) coding structure is proposed, and then an end-to-end distortion model with error resilient filter is used for mode decision in Macroblock (MB) level rate distortion cost decision, finally the number of header bits packet in HQF is determined in frame level rate cost decision. Experimental results show that the proposed method can achieve better performance than previous schemes.

This paper describes a sub 100-mW H.264/AVC [email protected] integer-pel motion estimation processor core for a low power video encoder. It supports macro block adaptive frame field (MBAFF) encoding and bi-directional prediction for a resolution of 1920times1080 pixels at 30 fps which haven't been realized by conventional methods. The proposed processor core features a novel hierarchical algorithm, a reconfigurable ring-connected systolic array architecture, and a segmentation- free rectangle-access search window buffer. The processor core has been designed in a 90 nm CMOS technology, and its core size is 2.5times2.5 mm2. With one core, one reference frame can be handled, and 48 mW is consumed at 1 V. Two-core configuration dissipates 96 mW for two reference frames.

Security and flexibility are two important issues in workflow management systems. The RBAC flexible workflow model is proposed recently which captures above needs, however there exist an open issue of its role hierarchies. With the numbers of users and roles increasing, how to reduce down the complexity of management is emerging concernful. A novel method of role management is presented in this paper that introduce the notation of partial inheritance into the role hierarchies. Compared with other dominating methods, it can efficiently achieve the objectives of role management inflexible workflow