Automated Skin Lesion Segmentation Via an Adaptive Dual Attention Module.

We present a convolutional neural network (CNN) equipped with a novel and efficient adaptive dual attention module (ADAM) for automated skin lesion segmentation from dermoscopic images, which is an essential yet challenging step for the development of a computer-assisted skin disease diagnosis system. The proposed ADAM has three compelling characteristics. First, we integrate two global context modeling mechanisms into the ADAM, one aiming at capturing the boundary continuity of skin lesion by global average pooling while the other dealing with the shape irregularity by pixel-wise correlation.

Embedding Carbon Nitride into a Covalent Organic Framework with Enhanced Photocatalysis Performance.

We report a new strategy to construct porous carbon nitride (PCN) by embedding a heptazine unit-the primary building block of carbon nitride-into the backbone of a covalent organic framework (COF). The strategy results in a new type of PCN which bears a fibrous morphology, high surface area and wide visible absorption. The photocatalytic performance was evaluated by photodegradation of an organic dye.