CISepsis: a causal inference framework for early sepsis detection.

Introduction: The early prediction of sepsis based on machine learning or deep learning has achieved good results.Most of the methods use structured data stored in electronic medical records, but the pathological characteristics of sepsis involve complex interactions between multiple physiological systems and signaling pathways, resulting in mixed structured data. Some researchers will introduce unstructured data when also introduce confounders.

Ovarian Ultrasound Image Segmentation Algorithm with Fused Multi-Scale Features.

Ultrasound imaging technology plays a vital role in medical imaging. Ovarian ultrasound image segmentation is challenging due to the wide variation in lesion sizes caused by the cancer detection period and individual differences, as well as the noise from reflected wave interference. To address these challenges, we propose an innovative algorithm for ovarian ultrasound image segmentation that incorporates multi-scale features.

T2TD: Text-3D Generation Model Based on Prior Knowledge Guidance.

In recent years, 3D models have been utilized in many applications, such as auto-drivers, 3D reconstruction, VR, and AR. However, the scarcity of 3D model data does not meet its practical demands. Thus, generating high-quality 3D models efficiently from textual descriptions is a promising but challenging way to solve this problem.

[Retrospective study on the modified Uhl technique of closed reduction and percutaneous pin in the treatment of Colles' fracture].

Objective: To retrospectively assess the advantages of the modified Uhl technique in the treatment of Colles' fracture guided by the principles of Chinese osteosynthesis (CO) concept.

Methods: A retrospective study was conducted on 358 patients with Colles' fracture treated with the modified Uhl technique of closed reduction and percutaneous pin between January 2016 and June 2021. Out of these, 120 eligible cases were selected and categorized into two groups according to different surgical methods:the closed reduction and percutaneous pin group, and the open reduction group.

Temporal-Spatial Correlation Attention Network for Clinical Data Analysis in Intensive Care Unit.

Recent advancements in medical information technology have enabled electronic health records (EHRs) to store comprehensive clinical data which has ushered healthcare into the era of "big data". However, medical data are rather complicated, making problem-solving in healthcare be limited in scope and comprehensiveness. The rapid development of deep learning in recent years has opened up opportunities for leveraging big data in healthcare.

Deep reinforcement learning framework for thoracic diseases classification via prior knowledge guidance.

The chest X-ray is commonly employed in the diagnosis of thoracic diseases. Over the years, numerous approaches have been proposed to address the issue of automatic diagnosis based on chest X-rays. However, the limited availability of labeled data for related diseases remains a significant challenge in achieving accurate diagnoses.

HMNet: Hierarchical Multi-Scale Brain Tumor Segmentation Network.

An accurate and efficient automatic brain tumor segmentation algorithm is important for clinical practice. In recent years, there has been much interest in automatic segmentation algorithms that use convolutional neural networks. In this paper, we propose a novel hierarchical multi-scale segmentation network (HMNet), which contains a high-resolution branch and parallel multi-resolution branches.

Toward Region-Aware Attention Learning for Scene Graph Generation.

Scene graph generation (SGGen) is a challenging task due to a complex visual context of an image. Intuitively, the human visual system can volitionally focus on attended regions by salient stimuli associated with visual cues. For example, to infer the relationship between man and horse, the interaction between human leg and horseback can provide strong visual evidence to predict the predicate ride.

DAN: Deep-Attention Network for 3D Shape Recognition.

Due to the wide applications in a rapidly increasing number of different fields, 3D shape recognition has become a hot topic in the computer vision field. Many approaches have been proposed in recent years. However, there remain huge challenges in two aspects: exploring the effective representation of 3D shapes and reducing the redundant complexity of 3D shapes.

Monocular Image-Based 3-D Model Retrieval: A Benchmark.

Monocular image-based 3-D model retrieval aims to search for relevant 3-D models from a dataset given one RGB image captured in the real world, which can significantly benefit several applications, such as self-service checkout, online shopping, etc. To help advance this promising yet challenging research topic, we built a novel dataset and organized the first international contest for monocular image-based 3-D model retrieval. Moreover, we conduct a thorough analysis of the state-of-the-art methods.

Deep Correlated Joint Network for 2-D Image-Based 3-D Model Retrieval.

In this article, we propose a novel deep correlated joint network (DCJN) approach for 2-D image-based 3-D model retrieval. First, the proposed method can jointly learn two distinct deep neural networks, which are trained for individual modalities to learn two deep nonlinear transformations for visual feature extraction from the co-embedding feature space. Second, we propose the global loss function for the DCJN, consisting of a discriminative loss and a correlation loss.

Sequential Saliency Guided Deep Neural Network for Joint Mitosis Identification and Localization in Time-Lapse Phase Contrast Microscopy Images.

The analysis of cell mitotic behavior plays important role in many biomedical research and medical diagnostic applications. To improve the accuracy of mitosis detection in automated analysis systems, this paper proposes the sequential saliency guided deep neural network (SSG-DNN) to jointly identify and localize mitotic events in time-lapse phase contrast microscopy images. It consists of three key modules.

Multi-Domain & Multi-Task Learning for Human Action Recognition.

Domain-invariant (view-invariant & modalityinvariant) feature representation is essential for human action recognition. Moreover, given a discriminative visual representation, it is critical to discover the latent correlations among multiple actions in order to facilitate action modeling. To address these problems, we propose a multi-domain & multi-task learning (MDMTL) method to (1) extract domain-invariant information for multi-view and multi-modal action representation and (2) explore the relatedness among multiple action categories.

[A comparative study of titanium elastic intramedullary nail internal fixation and bone plate internal fixation in the treatment of adult Galeazzi fracture].

Objective: To explore the effectiveness difference between titanium elastic intramedullary nail internal fixation and bone plate internal fixation in the treatment of adult Galeazzi fracture.

Methods: Ninety-seven patients of Galeazzi fracture according with the selection criteria were divided into 2 groups by prospective cohort study, who were admitted between January 2012 and November 2015. In the patients, 59 were treated with open reduction and bone plate internal fixation (plate group), and 38 with titanium elastic intramedullary nail internal fixation (minimally invasive group).

Multi-Grained Random Fields for Mitosis Identification in Time-Lapse Phase Contrast Microscopy Image Sequences.

This paper proposes a multi-grained random fields (MGRFs) model for mitosis identification. To deal with the difficulty in hidden state discovery and sequential structure modeling in mitosis sequences only containing gradual visual pattern changes, we design the graphical structure to transform individual sequence into a set of coarse-to-fine grained sequencesconveying diverse temporal dynamics. Furthermore, we propose the corresponding probabilistic model for joint temporal learning and feature learning.

View-Based 3-D Model Retrieval: A Benchmark.

View-based 3-D model retrieval is one of the most important techniques in numerous applications of computer vision. While many methods have been proposed in recent years, to the best of our knowledge, there is no benchmark to evaluate the state-of-the-art methods. To tackle this problem, we systematically investigate and evaluate the related methods by: 1) proposing a clique graph-based method and 2) reimplementing six representative methods.

Benchmarking a Multimodal and Multiview and Interactive Dataset for Human Action Recognition.

Human action recognition is an active research area in both computer vision and machine learning communities. In the past decades, the machine learning problem has evolved from conventional single-view learning problem, to cross-view learning, cross-domain learning and multitask learning, where a large number of algorithms have been proposed in the literature. Despite having large number of action recognition datasets, most of them are designed for a subset of the four learning problems, where the comparisons between algorithms can further limited by variances within datasets, experimental configurations, and other factors.

Multi-Modal Clique-Graph Matching for View-Based 3D Model Retrieval.

Multi-view matching is an important but a challenging task in view-based 3D model retrieval. To address this challenge, we propose an original multi-modal clique graph (MCG) matching method in this paper. We systematically present a method for MCG generation that is composed of cliques, which consist of neighbor nodes in multi-modal feature space and hyper-edges that link pairwise cliques.

Hierarchical Clustering Multi-Task Learning for Joint Human Action Grouping and Recognition.

This paper proposes a hierarchical clustering multi-task learning (HC-MTL) method for joint human action grouping and recognition. Specifically, we formulate the objective function into the group-wise least square loss regularized by low rank and sparsity with respect to two latent variables, model parameters and grouping information, for joint optimization. To handle this non-convex optimization, we decompose it into two sub-tasks, multi-task learning and task relatedness discovery.

Jointly Learning Multiple Sequential Dynamics for Human Action Recognition.

Discovering visual dynamics during human actions is a challenging task for human action recognition. To deal with this problem, we theoretically propose the multi-task conditional random fields model and explore its application on human action recognition. For visual representation, we propose the part-induced spatiotemporal action unit sequence to represent each action sample with multiple partwise sequential feature subspaces.

[Comparison between mini-traumatic bone-grafting and non-bone-grafting in percutaneous K-wire fixation to treat the calcaneal fractures].

Objective: To compare the effect between mini-traumatic bone-grafting and non-bone-grafting in percutaneous K-wire fixation for treating the calcaneal fractures.

Methods: From 2002 to 2006, 112 patients with the type II (Paley type) fractures of calcaneus were studied. There were 56 cases in bone-grafting group involving 36 males and 20 famales,aged from 21 to 65, averaged (42.