Recent advances in deep learning have led to a promising performance in many medical
image analysis tasks. As the most commonly performed radiological exam, chest …

In the last five years, deep learning has attracted great interest in computer-assisted systems
for Minimally Invasive Surgery. The straightforward accessibility to images in surgical …

In mainstream computer vision and machine learning, public datasets such as ImageNet,
COCO and KITTI have helped drive enormous improvements by enabling researchers to …

Data-driven computational approaches have evolved to enable extraction of information
from medical images with reliability, accuracy, and speed, which is already transforming …

Incremental few-shot semantic segmentation (IFSS) targets at incrementally expanding
model's capacity to segment new class of images supervised by only a few samples …

Automatic instrument segmentation in video is an essentially fundamental yet challenging
problem for robot-assisted minimally invasive surgery. In this paper, we propose a novel …

Background and objective: Surgical tool detection, segmentation, and 3D pose estimation
are crucial components in Computer-Assisted Laparoscopy (CAL). The existing frameworks …

Turbine blades can only be detected nondestructively and precisely using industrial
computed tomography (CT). The accuracy of CT image segmentation, which is a key step in …

In this article, we present a novel hybrid framework, which integrates spatial-temporal
semantic segmentation with postclassification relearning, for multitemporal land use and …

Precise instrument segmentation aids surgeons to navigate the body more easily and
increases patient safety. While accurate tracking of surgical instruments in real-time plays a …