In domains such as medical and healthcare, the interpretability and explainability of
machine learning and artificial intelligence systems are crucial for building trust in their …

Radiomics and genomics represent two of the most promising fields of cancer research,
designed to improve the risk stratification and disease management of patients with prostate …

There have been considerable debates over 2D and 3D representation learning on 3D
medical images. 2D approaches could benefit from large-scale 2D pretraining, whereas they …

Abstract Purpose Low-Dose Computed Tomography (LDCT) is the most common imaging
modality for lung cancer diagnosis. The presence of nodules in the scans does not …

Recent evolution in deep learning has proven its value for CT-based lung nodule
classification. Most current techniques are intrinsically black-box systems, suffering from two …

Manual rib inspections in computed tomography (CT) scans are clinically critical but labor-
intensive, as 24 ribs are typically elongated and oblique in 3D volumes. Automatic rib …

Diagnosis and treatment of multiple pulmonary nodules are clinically important but
challenging. Prior studies on nodule characterization use solitary-nodule approaches on …

This paper addresses a fundamental challenge in 3D medical image processing: how to
deal with imaging thickness. For anisotropic medical volumes, there is a significant …

Rib fractures are a common and potentially severe injury that can be challenging and labor-
intensive to detect in CT scans. While there have been efforts to address this field, the lack of …

Pulmonary nodules and masses are crucial imaging features in lung cancer screening that
require careful management in clinical diagnosis. Despite the success of deep learning …