Simple Summary This study investigates the latest achievements, challenges, and future
research directions of deep learning techniques for lung cancer and pulmonary nodule …

Lung cancer (LC) remains a leading cause of death worldwide. Early diagnosis is critical to
protect innocent human lives. Computed tomography (CT) scans are one of the primary …

The integration of positron emission tomography (PET) and single-photon emission
computed tomography (SPECT) imaging techniques with machine learning (ML) algorithms …

Background Medical image analysis pipelines often involve segmentation, which requires a
large amount of annotated training data, which is time-consuming and costly. To address …

High-quality and fast reconstructions are essential for the clinical application of positron
emission tomography (PET) imaging. Herein, a deep-learning-based framework is proposed …

Tumor lesion segmentation and staging in cancer patients are one of the most challenging
tasks for radiologists to recommend better treatment planning like radiation therapy …

Recent advances in deep learning have contributed greatly to the field of parallel MR
imaging, where a reduced amount of k-space data are acquired to accelerate imaging time …

Potholes on the road surface are a significant safety hazard and can cause severe damage
to vehicles. Identifying and repairing potholes is a challenging task that requires efficient and …

Objective. Radiation therapy for head and neck (H&N) cancer relies on accurate
segmentation of the primary tumor. A robust, accurate, and automated gross tumor volume …

Objective. Deep learning networks such as convolutional neural networks (CNN) and
Transformer have shown excellent performance on the task of medical image segmentation …