Latest AI and machine learning research in smoking & tobacco for healthcare professionals.
Biological neural networks contain diverse cell types with heterogeneous electrophysiological proper...
Radiotherapy (RT) dose optimization is often labor-intensive, requiring repeated manual adjustments ...
Develop a neighborhood-level framework using machine learning and causal inference to identify socio...
In digital pathology, blurriness in whole slide images (WSI) is a common issue, with severe blurrine...
Multiclass segmentation of microanatomy in kidney biopsies is an important and non-trivial task in c...
The propagation of tobacco-related misinformation significantly impacts public health, particularly ...
Social and behavioral determinants of health (SBDH) are increasingly recognized as essential for pro...
In cardiovascular magnetic resonance (CMR), myocardial native T1 mapping enables quantitative, non-i...
For adult diffuse gliomas (ADGs), most grading can be achieved through molecular subtyping, retainin...
Clinical monitoring in the most vulnerable patients such as newborns relies on invasive and costly p...
Postoperative atrial fibrillation (POAF) affects 20 to 50% of patients undergoing cardiac surgery an...
Accurate molecular profiling and prognostication from routine histopathology slides could transform ...
Should original research routinely contain prominent policy claims, such as recommendations for poli...
Replication timing is a costly but powerful tool for characterizing cellular mechanisms that underli...
Cancer remains one of the most significant global health challenges. De-spite advances in treatment,...
We propose a novel explainable AI (XAI) model for classification tasks that can treat multi-modal an...
Oral squamous cell carcinoma (OSCC) remains the most prevalent neoplasm of the head and neck. In rec...
Recently, zero-shot image captioning has gained increasing attention, where only text data is avai...
Existing unsupervised distillation-based methods rely on the differences between encoded and decod...
Although mainstream unsupervised anomaly detection (AD) (including image-level classification and ...
We obtain the first analytic, interpretable and predictive theory of creativity in convolutional d...