In recent years, there has been a growing emphasis on assessing and ensuring the quality
of horticultural and agricultural produce. Traditional methods involving field measurements …

One of the primary challenges in applying deep learning approaches to medical imaging is
the limited availability of data due to various factors. These factors include concerns about …

Abstract In recent years, Machine Learning approaches (ML) have shown promising results
in addressing many tasks in medical image analysis. In particular, the analysis of Bone …

With the increase in the aging population of many countries, the prevalence of neovascular
age-related macular degeneration (nAMD) is expected to increase. Morphological …

In recent years, the encoder–decoder U-shaped network architecture has become a
mainstream structure for medical image segmentation. Its biggest advantage lies in the …

Artificial intelligence-based segmentation models can assist the early-stage detection of
lung COVID-19 infections or lesions from medical images with higher efficiency versus …

The two major challenges to deep-learning-based medical image segmentation are multi-
modality and a lack of expert annotations. Existing semi-supervised segmentation models …

Since the onset of the Covid-19 pandemic in late 2019 the realm of medical image analysis
has seen a surge in importance particularly with the utilization of CT-scan imaging for …

The accurate segmentation of clustered microcalcifications in mammography is crucial for
the diagnosis and treatment of breast cancer. Despite exhibiting expert-level accuracy …

Abstract Background and Objective Lung tumor annotation is a key upstream task for further
diagnosis and prognosis. Although deep learning techniques have promoted automation of …