HID-CON: weakly supervised intrahepatic cholangiocarcinoma subtype classification of whole slide images using contrastive hidden class detection.

Purpose: Biliary tract cancer, also known as intrahepatic cholangiocarcinoma (IHCC), is a rare disease that shows no clear symptoms during its early stage, but its prognosis depends highly on the cancer subtype. Hence, an accurate cancer subtype classification model is necessary to provide better treatment plans to patients and to reduce mortality. However, annotating histopathology images at the pixel or patch level is time-consuming and labor-intensive for giga-pixel whole slide images.

Deep Learning for Plant Species Classification Using Leaf Vein Morphometric.

An automated plant species identification system could help botanists and layman in identifying plant species rapidly. Deep learning is robust for feature extraction as it is superior in providing deeper information of images. In this research, a new CNN-based method named D-Leaf was proposed.

A genetic programming approach to oral cancer prognosis.

Background: The potential of genetic programming (GP) on various fields has been attained in recent years. In bio-medical field, many researches in GP are focused on the recognition of cancerous cells and also on gene expression profiling data. In this research, the aim is to study the performance of GP on the survival prediction of a small sample size of oral cancer prognosis dataset, which is the first study in the field of oral cancer prognosis.

[Construction of combined site-directed random mutation libraries of recombinant human lymphotoxin].

To construct the combined site-directed random mutation library of recombinant human Lymphotoxin (rhLT) for in vitro molecular evolution study, and to study the structure and function relationship. The random point mutations at the sites of 46,106 and 130 were individually generated by overlap PCR amplification with the random nucleotide primers. The three point mutations were combined and cloned into pMD-18T vector to construct the combined mutation library.