A Degradable Bioelectronic Scaffold for Localized Cell Transfection toward Enhancing Wound Healing in a 3D Space.

Large skin wounds, with extensive surface area and deep vertical full-thickness involvement, can pose significant challenges in clinical settings. Traditional routes for repairing skin wounds encompass three hallmarks: 1) scab formation for hemostasis; 2) proliferation and migration of epidermal cells for wound closure; 3) proliferation, migration, and functionalization of fibroblasts and endothelial cells for dermal remodeling. However, this route face remarkable challenges to healing large wounds, usually leading to disordered structures and loss of functions in the regenerated skin, due to limited control on the transition among the three stages.

Automated diagnosis of anterior cruciate ligament via a weighted multi-view network.

To build a three-dimensional (3D) deep learning-based computer-aided diagnosis (CAD) system and investigate its applicability for automatic detection of anterior cruciate ligament (ACL) of the knee joint in magnetic resonance imaging (MRI). In this study, we develop a 3D weighted multi-view convolutional neural network by fusing different views of MRI to detect ACL. The network is evaluated on two MRI datasets, the in-house MRI-ACL dataset and the publicly available MRNet-v1.

MVCNet: Multiview Contrastive Network for Unsupervised Representation Learning for 3-D CT Lesions.

With the renaissance of deep learning, automatic diagnostic algorithms for computed tomography (CT) have achieved many successful applications. However, they heavily rely on lesion-level annotations, which are often scarce due to the high cost of collecting pathological labels. On the other hand, the annotated CT data, especially the 3-D spatial information, may be underutilized by approaches that model a 3-D lesion with its 2-D slices, although such approaches have been proven effective and computationally efficient.

Multi-task contrastive learning for automatic CT and X-ray diagnosis of COVID-19.

Computed tomography (CT) and X-ray are effective methods for diagnosing COVID-19. Although several studies have demonstrated the potential of deep learning in the automatic diagnosis of COVID-19 using CT and X-ray, the generalization on unseen samples needs to be improved. To tackle this problem, we present the contrastive multi-task convolutional neural network (CMT-CNN), which is composed of two tasks.