Six-Center Assessment of CNN-Transformer with Belief Matching Loss for Patient-Independent Seizure Detection in EEG

Neurologists typically identify epileptic seizures from electroencephalograms (EEGs) by visual inspection. This process is often time-consuming, especially for EEG recordings that last hours or days. To expedite the process, a reliable, automated, and patient-independent seizure detector is essential. However, developing a patient-independent...

Analyzing Components of a Transformer under Different Data Scales in 3D Prostate CT Segmentation

Literature on medical imaging segmentation claims that self-attention-based Transformer blocks perform better than convolution in UNet-based architectures. This recently touted success of Transformers warrants an investigation into which of its components contribute to its performance. Moreover, previous work has a limitation of analysis only at...