pytorch分类

To develop and validate a deep learning model for the detection of aberrant anterior tibial artery (AATA) on axial T2-weighted knee MRI, given the surgical relevance of unrecognized AATA and the lack of automated detection tools.

Candidemia is a rare but life-threatening bloodstream infection that remains difficult to predict using conventional risk stratification approaches, highlighting the need for improved predictive strategies. As a result, empiric antifungal therapy is often delayed even in high-risk patients.

Detector arrays are commonly used for treatment plan verifications in intensity modulated radiation therapy. However, the intrinsic resolution of detector arrays is limited by the physical dimensions of each single detector and the detector-to-detector distance. This may lead to inaccurate representations of steep gradients and narrow dose peaks.

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.