Despite the remarkable performance of deep learning methods on various tasks, most
cutting-edge models rely heavily on large-scale annotated training examples, which are …

Precise determination and assessment of bladder cancer (BC) extent of muscle invasion
involvement guides proper risk stratification and personalized therapy selection. In this …

Today, deep convolutional neural networks (CNNs) have demonstrated state of the art
performance for supervised medical image segmentation, across various imaging modalities …

Fundus images have been widely used in routine examinations of ophthalmic diseases. For
some diseases, the pathological changes mainly occur around the optic disc area; therefore …

Abstract Domain adaptation (DA) has drawn high interest for its capacity to adapt a model
trained on labeled source data to perform well on unlabeled or weakly labeled target data …

The purpose of federated learning is to enable multiple clients to jointly train a machine
learning model without sharing data. However, the existing methods for training an image …

Deep-learning-based registration methods emerged as a fast alternative to conventional
registration methods. However, these methods often still cannot achieve the same …

Abstract Although Deep Neural Networks (DNNs) have great generalization and prediction
capabilities, their functioning does not allow a detailed explanation of their behavior …

Weakly supervised learning has emerged as an appealing alternative to alleviate the need
for large labeled datasets in semantic segmentation. Most current approaches exploit class …

A versatile medical image segmentation model applicable to images acquired with diverse
equipment and protocols can facilitate model deployment and maintenance. However …