Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

Motivation Medical image analysis involves a series of tasks used to assist physicians in
qualitative and quantitative analyses of lesions or anatomical structures which can …

Deep reinforcement learning (DRL) and evolution strategies (ESs) have surpassed human-
level control in many sequential decision-making problems, yet many open challenges still …

Simple Summary In soft-tissue sarcoma (STS) patients, the decision for the optimal treatment
modality largely depends on STS size, location, and a pathological measure that assesses …

Objectives To determine the accuracy of an artificial neural network (ANN) for fully
automated detection of the presence and phase of iodinated contrast agent in routine …

Reinforcement learning takes sequential decision-making approaches by learning the policy
through trial and error based on interaction with the environment. Combining deep learning …

Accurate detection of anatomical landmarks is an essential step in several medical imaging
tasks. We propose a novel communicative multi-agent reinforcement learning (C-MARL) …

Vertebral compression fractures often go clinically undetected and consequently untreated,
resulting in severe secondary fractures due to osteoporosis, and potentially leading to …

This paper discusses current methods and trends for 3D bounding box detection in
volumetric medical image data. For this purpose, an overview of relevant papers from recent …

Nowadays, breast and cervical cancers are respectively the first and fourth most common
causes of cancer death in females. It is believed that, automated systems based on artificial …