The domain shift between training and testing data presents a significan...
Previous deep learning efforts have focused on improving the performance...
Deep Learning methods have recently seen increased adoption in medical
i...
Since the emergence of convolutional neural networks (CNNs), and later v...
Medical image segmentation is a vital healthcare endeavor requiring prec...
The ability to automatically detect and track surgical instruments in
en...
Federated learning (FL) is a distributed machine learning (ML) approach ...
The number of studies on deep learning for medical diagnosis is expandin...
Vestibular schwannoma (VS) is a non-cancerous tumor located next to the ...
Deep learning models have been effective for various fetal ultrasound
se...
The performance of the Deep Learning (DL) models depends on the quality ...
Chest X-ray is one of the most popular medical imaging modalities due to...
Lung cancer is a leading cause of death worldwide. Early-stage detection...
Vision Transformer (ViT), a radically different architecture than
convol...
We have gained access to vast amounts of multi-omics data thanks to Next...
When oncologists estimate cancer patient survival, they rely on multimod...
Learning spatiotemporal features is an important task for efficient vide...
Vision Transformers (ViT) are competing to replace Convolutional Neural
...
Deep learning is showing an increasing number of audience in medical ima...
Nuclei segmentation and classification is the first and most crucial ste...
Accurate prognosis of a tumor can help doctors provide a proper course o...
For personalized medicines, very crucial intrinsic information is presen...
Despite the introduction of vaccines, Coronavirus disease (COVID-19) rem...
To assess fetal health during pregnancy, doctors use the gestational age...
Cancer is one of the leading causes of death worldwide, and head and nec...
Glioblastoma is a common brain malignancy that tends to occur in older a...
The value of quick, accurate, and confident diagnoses cannot be undermin...
Contrastive learning has proven useful in many applications where access...