Li-SegPNet: Encoder-Decoder Mode Lightweight Segmentation Network for Colorectal Polyps Analysis.

Objective: One of the fundamental and crucial tasks for the automated diagnosis of colorectal cancer is the segmentation of the acute gastrointestinal lesions, most commonly colorectal polyps. Therefore, in this work, we present a novel lightweight encoder-decoder mode of architecture with the attention mechanism to address this challenging task.

Methods: The proposed Li-SegPNet architecture harnesses cross-dimensional interaction in feature maps with novel encoder block with modified triplet attention.

A unified NMR strategy for high-throughput determination of backbone fold of small proteins.

An efficient semi-automated strategy called PFBD (i.e. Protein Fold from Backbone Data only) has been presented for rapid backbone fold determination of small proteins.