Simple Summary Artificial intelligence (AI) technology has been advancing rapidly in recent
years and is being implemented in society. The medical field is no exception, and the clinical …

Deep learning has demonstrated remarkable performance across various tasks in medical
imaging. However, these approaches primarily focus on supervised learning, assuming that …

Multi-source domain adaptation (MDA) aims to transfer knowledge from multiple source
domains to an unlabeled target domain. MDA is a challenging task due to the severe …

Today, artificial intelligence and deep learning techniques constitute a prominent part in the
area of medical sciences. These techniques help doctors detect diseases early and reduce …

Multi-class cell detection and counting is an essential task for many pathological diagnoses.
Manual counting is tedious and often leads to inter-observer variations among pathologists …

In recent years, federated learning has been believed to play a considerable role in cross-
silo scenarios (eg, medical institutions) due to its privacy-preserving properties. However …

Automating blood cell counting and detection from smear slides holds significant potential
for aiding doctors in disease diagnosis through blood tests. However, existing literature has …

Abstract Domain shift refers to change of distributional characteristics between the training
(source) and the testing (target) datasets of a learning task, leading to performance drop. For …

Hematologic diseases and blood disorders can be studied through microscopic examination
of blood smear images or chemical assays. Many researchers are focused on utilizing deep …

Leukemia, a malignant disease characterized by the rapid proliferation of specific types of
white blood cells (WBC), has prompted increased interest in leveraging automatic WBC …