FEEDNet: a feature enhanced encoder-decoder LSTM network for nuclei instance segmentation for histopathological diagnosis.

Automated cell nuclei segmentation is vital for the histopathological diagnosis of cancer. However, nuclei segmentation from 'hematoxylin and eosin' (HE) stained 'whole slide images' (WSIs) remains a challenge due to noise-induced intensity variations and uneven staining. The goal of this paper is to propose a novel deep learning model for accurately segmenting the nuclei in HE-stained WSIs.

Emergence of nano silica for oil and gas well cementing: application, challenges, and future scope.

Nanotechnology has opened up a plethora of opportunities and has acquired extreme importance in a myriad of fields to produce enhanced materials. Their special properties make them sustainable for industrial purposes. One of the most crucial processes in the petroleum and geothermal industries is cementing.