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A comprehensive evaluation of histopathology foundation models for ovarian cancer subtype classification.

NPJ Precis Oncol

January 2025

Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK.

Histopathology foundation models show great promise across many tasks, but analyses have been limited by arbitrary hyperparameters. We report the most rigorous single-task validation study to date, specifically in the context of ovarian carcinoma morphological subtyping. Attention-based multiple instance learning classifiers were compared using three ImageNet-pretrained encoders and fourteen foundation models, each trained with 1864 whole slide images and validated through hold-out testing and two external validations (the Transcanadian Study and OCEAN Challenge).

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With the rapid development of Internet of Things (IoT) technology, embedded devices in various computer vision scenarios can realize real-time target detection and recognition tasks, such as intelligent manufacturing, automatic driving, smart home, and so on. YOLOv8, as an advanced deep learning model in the field of target detection, has attracted much attention for its excellent detection speed, high precision, and multi-task processing capability. However, since IoT embedded devices typically own limited computing resources, direct deployment of YOLOv8 is a big challenge, especially for real-time detection tasks.

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The detection of rare or deviant stimuli shares common brain circuits involved in temporal processing and salience, critical for cognitive control. Disruption in these processes may contribute to the mechanisms of the disease and explain cognitive deficits observed in psychosis and related disorders. We designed a neuroimaging study, using oddball task-based functional sequences (fMRI) and diffusion tensor imaging (DTI), comparing healthy controls (HC, n = 14, 7 females) and patients with stable psychosis (PSY, n = 20, 10 females).

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This meta-review provides the first meta-analytic evidence from published meta-analyses examining the effectiveness of acute exercise interventions on cognitive function. A multilevel meta-analysis with a random-effects model and tests of moderators were performed in R. Thirty systematic reviews with meta-analyses (383 unique studies with 18,347 participants) were identified.

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The interplay between individual capacities and pair performance according to the experience in Acrobatic Gymnastics.

Sports Biomech

January 2025

Centre for Research, Education, Innovation and Intervention in Sport (CIFI2D), Faculty of Sports, University of Porto, Porto, Portugal.

The interplay between individual capacities and group performance provides insights for different tasks and contexts. So far, little is known about the individual capacities of base and top gymnasts and mechanical efficiency during pair tasks of Acrobatic Gymnastics. This work aims to investigate: (1) the effect of the pair experience in the mechanical efficiency during a pair task; (2) the effect of the individual training experience in the gymnasts' individual capacities, and (3) the contribution of individual capacities and pair mechanical efficiency to the performance of a partner-assisted flight task.

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