Gradient-aware learning for joint biases: Label noise and class imbalance.

Data biases such as class imbalance and label noise always exist in large-scale datasets in real-world. These problems bring huge challenges to deep learning methods. Some previous works adopted loss re-weighting, sample re-weighting, or data-dependent regularization to mitigate the influence of these training biases.

Complex carotid artery segmentation in multi-contrast MR sequences by improved optimal surface graph cuts based on flow line learning.

Carotid atherosclerosis is one of the leading causes of cardiovascular disease with high mortality. Multi-contrast MRI can identify atherosclerotic plaque components with high sensitivity and specificity. Accurate segmentation of the diseased carotid artery from MR images is very essential to quantitatively evaluate the state of atherosclerosis.

Trichosanthin inhibits cervical cancer by regulating oxidative stress-induced apoptosis.

Based on many studies, trichosanthin (TCS) has an antiviral effect that regulates immune response, and targets cancer cells to exert broad-spectrum anti-tumor pharmacological activities. It is speculated that TCS may be a potential natural active drug for preventing as well as treating cervical cancer. But the clearer impact along with underlying TCS mechanism on cervical cancer are still unclear.

Cascaded residual U-net for fully automatic segmentation of 3D carotid artery in high-resolution multi-contrast MR images.

Accurate and automatic carotid artery segmentation for magnetic resonance (MR) images is eagerly expected, which can greatly assist a comprehensive study of atherosclerosis and accelerate the translation. Although many efforts have been made, identification of the inner lumen and outer wall in diseased vessels is still a challenging task due to complex vascular deformation, blurred wall boundary, and confusing componential expression. In this paper, we introduce a novel fully automatic 3D framework for simultaneously segmenting the carotid artery from high-resolution multi-contrast MR sequences based on deep learning.

Automatic centerline extraction of cerebrovascular in 4D CTA based on tubular features.

Vascular centerlines have crucial significance in reconstruction, registration, segmentation and vascular parameter analysis. The extraction of vessel structures remains a difficult problem in the completeness and continuity of results. In this paper, we present a novel method to extract cerebrovascular centerlines from four-dimensional computed tomography angiography images.