Skin lesion classification using features of 3D border lines.

Machine learning algorithms are progressively assuming important roles as computational tools to support clinical diagnosis, namely in the classification of pigmented skin lesions using RGB images. Most current classification methods rely on common 2D image features derived from shape, colour or texture, which does not always guarantee the best results. This work presents a contribution to this field, by exploiting the lesions' border line characteristics using a new dimension - depth, which has not been thoroughly investigated so far.

Melanoma classification using light-Fields with morlet scattering transform and CNN: Surface depth as a valuable tool to increase detection rate.

Medical image classification through learning-based approaches has been increasingly used, namely in the discrimination of melanoma. However, for skin lesion classification in general, such methods commonly rely on dermoscopic or other 2D-macro RGB images. This work proposes to exploit beyond conventional 2D image characteristics, by considering a third dimension (depth) that characterises the skin surface rugosity, which can be obtained from light-field images, such as those available in the SKINL2 dataset.

Light Field Image Dataset of Skin Lesions.

Light field imaging technology has been attracting increasing interest because it enables capturing enriched visual information and expands the processing capabilities of traditional 2D imaging systems. Dense multiview, accurate depth maps and multiple focus planes are examples of different types of visual information enabled by light fields. This technology is also emerging in medical imaging research, like dermatology, allowing to find new features and improve classification algorithms, namely those based on machine learning approaches.

Stability and Ligand Promiscuity of Type A Carbohydrate-binding Modules Are Illustrated by the Structure of CBM64C.

Deconstruction of cellulose, the most abundant plant cell wall polysaccharide, requires the cooperative activity of a large repertoire of microbial enzymes. Modular cellulases contain non-catalytic type A carbohydrate-binding modules (CBMs) that specifically bind to the crystalline regions of cellulose, thus promoting enzyme efficacy through proximity and targeting effects. Although type A CBMs play a critical role in cellulose recycling, their mechanism of action remains poorly understood.