Deep learning models have revolutionized numerous fields, yet their decision-making
processes often remain opaque, earning them the characterization of “black-box” models …

Computed tomography (CT) is used in a wide range of medical imaging diagnoses.
However, the reconstruction of CT images from raw projection data is inherently complex …

The application of machine learning to tasks involving volumetric biomedical imaging is
constrained by the limited availability of annotated datasets of three-dimensional (3D) scans …

The lack of drowning-specific pathological findings postmortem complicates medico-legal
investigations when bodies are recovered in water. This review provides an in-depth …

Parkinson's Disease (PD) is a progressive neurodegenerative disorder with substantial
impact on patients' quality of life. Subthalamic nucleus deep brain stimulation (STN-DBS) is …

In forensic medicine, fatal hypothermia diagnosis is not always easy because findings are
not specific, especially if traumatized. Post-mortem computed tomography (PMCT) is a …

Graves' orbitopathy is an inflammatory disorder that causes changes in different structures
close to the eye. Accurate and consistent diagnoses are essential to improve the quality of …

The decline in traditional autopsy practices has led to the rise of autopsy imaging as a non-
invasive alternative. However, the shortage of forensic pathologists skilled in postmortem …

Addressing the challenge of compromised imaging quality in sparse Computed Tomography
(CT) reconstructions—a significant issue due to the inherent sparsity of CT scan data—we …

Deep learning has proven to be a powerful tool for analyzing complex medical data;
however, its effectiveness can be hindered by limited training data, leading to overfitting. In …