Recently, deep learning, especially convolutional neural networks, has achieved the
remarkable results in natural image classification and segmentation. At the same time, in the …

Even though convolutional neural networks (CNNs) are driving progress in medical image
segmentation, standard models still have some drawbacks. First, the use of multi-scale …

Deep learning, a new branch of machine learning algorithm, has emerged as a fast growing
trend in medical imaging and become the state-of-the-art method in various clinical …

Abstract Recent advances in Convolutional Neural Network (CNN) model interpretability
have led to impressive progress in visualizing and understanding model predictions. In …

Deep learning empowers the mainstream medical image segmentation methods.
Nevertheless, current deep segmentation approaches are not capable of efficiently and …

Multimodal positron emission tomography-computed tomography (PET-CT) is used routinely
in the assessment of cancer. PET-CT combines the high sensitivity for tumor detection of …

Gross tumor volume (GTV) and clinical target volume (CTV) delineation are two critical steps
in the cancer radiotherapy planning. GTV defines the primary treatment area of the gross …

OAR segmentation is a critical step in radiotherapy of head and neck (H&N) cancer, where
inconsistencies across radiation oncologists and prohibitive labor costs motivate automated …

Liver lesion segmentation is an essential process to assist doctors in hepatocellular
carcinoma diagnosis and treatment planning. Multi-modal positron emission tomography …

Purpose: More and more automatic segmentation tools are being introduced in routine
clinical practice. However, physicians need to spend a considerable amount of time in …