Pytorch 架构

To improve slice profile consistency across echo trains in turbo spin echo (TSE) imaging, thereby reducing image blurring and increasing the accuracy of multi echo spin echo

To develop a machine-learning model that integrates routine clinical parameters with tumor mutational burden (TMB) and to evaluate its performance in predicting responses to programmed death-1 (PD-1)/programmed death-ligand 1(PD-L1) inhibitors across various cancer types.

Postural orthostatic tachycardia syndrome (POTS) is a common autonomic disorder characterized by orthostatic intolerance and excessive tachycardia upon standing. Despite its prevalence, POTS is often underdiagnosed or diagnosed late, largely due to limited access to autonomic specialists and testing. This study aimed to evaluate the performance of machine learning (ML) models in diagnosing POTS using validated symptom surveys and physiological measurements.

Acromegaly poses clinical challenges in terms of early diagnosis and intervention. Therefore, the development of novel diagnostic tools is essential. Although artificial intelligence (AI) models based on external appearance have been proposed, privacy concerns have limited their use.

I show that a software framework intended primarily for training of neural networks, PyTorch, is easily applied to a general function minimisation problem in science. The qualities of PyTorch of ease-of-use and very high efficiency are found to be applicable in this domain and lead to two orders of magnitude improvement in time-to-solution with very small software engineering effort.

Torchattacks is a PyTorch library that contains adversarial attacks to generate adversarial examples and to verify the robustness of deep learning models. The code can be found at https://github.com/Harry24k/adversarial-attacks-pytorch.

The manual identification of dental implant systems on radiographs is time-consuming, operator-dependent, and prone to diagnostic inaccuracies, particularly for patients where clinical documentation is lacking. The increasing variety of implant designs further complicates identification in prosthetic and surgical practice.

Periodontitis is a common chronic inflammatory disease closely associated with metabolic syndrome. The triglyceride-glucose (TyG) index is a surrogate marker of insulin resistance. However, its relationship with periodontitis remains underexplored. This study aims to utilise the large national database (NHANES) and explore the predictive value of TyG index for periodontitis through a deep learning model, and to clarify its correlation.

Osteoarthritis (OA) is a degenerative joint disease characterized by progressive cartilage loss, bone remodeling, and chronic pain. The growing global burden of OA motivates the evaluation of artificial intelligence (AI) approaches for automating radiographic diagnosis.

Cardiogenic shock (CS) in patients undergoing percutaneous coronary intervention (PCI) involves rapidly changing clinical, hemodynamic, and metabolic factors that current models cannot effectively integrate.