Knowledge Distillation in Video-Based Human Action Recognition: An Intuitive Approach to Efficient and Flexible Model Training.

Training a model to recognize human actions in videos is computationally intensive. While modern strategies employ transfer learning methods to make the process more efficient, they still face challenges regarding flexibility and efficiency. Existing solutions are limited in functionality and rely heavily on pretrained architectures, which can restrict their applicability to diverse scenarios.

A Rare Complication of Henoch-Schönlein Purpura/IgA Vasculitis in an Adult Woman After COVID-19 Infection.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can affect multiple organs due to activation of an inflammatory response. One of the key components of this response is the activation of immunoglobulin A (IgA), thus causing endothelial injury and inflammation. Henoch-Schönlein purpura (HSP) has been rarely reported in adult patients as a complication of the coronavirus disease 2019 (COVID-19) infection.

Real-time instance segmentation of surgical instruments using attention and multi-scale feature fusion.

Precise instrument segmentation aids surgeons to navigate the body more easily and increases patient safety. While accurate tracking of surgical instruments in real-time plays a crucial role in minimally invasive computer-assisted surgeries, it is a challenging task to achieve, mainly due to: (1) a complex surgical environment, and (2) model design trade-off in terms of both optimal accuracy and speed. Deep learning gives us the opportunity to learn complex environment from large surgery scene environments and placements of these instruments in real world scenarios.

Assessing YOLACT++ for real time and robust instance segmentation of medical instruments in endoscopic procedures.

Image-based tracking of laparoscopic instruments plays a fundamental role in computer and robotic-assisted surgeries by aiding surgeons and increasing patient safety. Computer vision contests, such as the Robust Medical Instrument Segmentation (ROBUST-MIS) Challenge, seek to encourage the development of robust models for such purposes, providing large, diverse, and high-quality datasets. To date, most of the existing models for instance segmentation of medical instruments were based on two-stage detectors, which provide robust results but are nowhere near to the real-time, running at 5 frames-per-second (fps) at most.

[Usefulness of Gradation Scales in the Surgical Treatment of Cerebral Arteriovenous Malformations].

Introduction: Brain arteriovenous malformations (AVM) are a complex disease responsible for up to 38% of hemorrhages in patients between 15-45 years old, carrying every bleeding episode a 25-50% risk of morbidity and a 10-20% of mortality. The therapeutic decision in a patient with an AVM needs to consider both the risks of the intervention and the risks of the natural evolution of the disease.

Objective: To assess the effectiveness of different AVM grading scales in predicting surgical risks according to our experience in a case serie.

Effective and Generalizable Graph-Based Clustering for Faces in the Wild.

Face clustering is the task of grouping unlabeled face images according to individual identities. Several applications require this type of clustering, for instance, social media, law enforcement, and surveillance applications. In this paper, we propose an effective graph-based method for clustering faces in the wild.