The full acceptance of Deep Learning (DL) models in the clinical field is rather low with
respect to the quantity of high-performing solutions reported in the literature. End users are …

A model's ability to express its own predictive uncertainty is an essential attribute for
maintaining clinical user confidence as computational biomarkers are deployed into real …

Generalizability of deep-learning (DL) model performance is not well understood and uses
anecdotal assumptions for increasing training data to improve segmentation of medical …

深度学习算法在语义分割领域已经取得大量突破, 对这些算法的性能评估应选择标准, 通用,
全面的度量指标, 以保证评价的客观性和有效性. 通过对当前语义分割评价指标和度量方法进行 …

Tools, models and statistical methods for signal processing and medical image analysis and
training deep learning models to create research prototypes for eventual clinical …

Convolutional Neural Networks (CNNs) have demonstrated remarkable performance in
medical image segmentation tasks, with the U-Net architecture being a prominent example …

Background: This paper focuses on segmenting the exact location of endometriosis using
the state-of-art technique known as U-Net. Endometriosis is a progressive disorder that has …

The rising incidence of cancer diagnoses necessitates efficient tumor detection methods in
CT scans. Manual tumor identification by physicians is labor-intensive and demands high …

Lung ultrasound imaging has become an important diagnostic tool for various respiratory
conditions. Deep learning models have shown impressive results in classifying …