Generative Adversarial Network Combined with SE-ResNet and Dilated Inception Block for Segmenting Retinal Vessels.

This study develops an accurate method based on the generative adversarial network (GAN) that targets the issue of the current discontinuity of micro vessel segmentation in the retinal segmentation images. The processing of images has become increasingly efficient since the advent of deep learning method. We have proposed an improved GAN combined with SE-ResNet and dilated inception block for the segmenting retinal vessels (SAD-GAN).

SRV-GAN: A generative adversarial network for segmenting retinal vessels.

In the field of ophthalmology, retinal diseases are often accompanied by complications, and effective segmentation of retinal blood vessels is an important condition for judging retinal diseases. Therefore, this paper proposes a segmentation model for retinal blood vessel segmentation. Generative adversarial networks (GANs) have been used for image semantic segmentation and show good performance.

MRF-IUNet: A Multiresolution Fusion Brain Tumor Segmentation Network Based on Improved Inception U-Net.

The automatic segmentation method of MRI brain tumors uses computer technology to segment and label tumor areas and normal tissues, which plays an important role in assisting doctors in the clinical diagnosis and treatment of brain tumors. This paper proposed a multiresolution fusion MRI brain tumor segmentation algorithm based on improved inception U-Net named MRF-IUNet (multiresolution fusion inception U-Net). By replacing the original convolution modules in U-Net with the inception modules, the width and depth of the network are increased.

Learning rich features with hybrid loss for brain tumor segmentation.

Background: Accurately segment the tumor region of MRI images is important for brain tumor diagnosis and radiotherapy planning. At present, manual segmentation is wildly adopted in clinical and there is a strong need for an automatic and objective system to alleviate the workload of radiologists.

Methods: We propose a parallel multi-scale feature fusing architecture to generate rich feature representation for accurate brain tumor segmentation.

The application of convolution neural network based cell segmentation during cryopreservation.

For most of the cells, water permeability and plasma membrane properties play a vital role in the optimal protocol for successful cryopreservation. Measuring the water permeability of cells during subzero temperature is essential. So far, there is no perfect segmentation technique to be used for the image processing task on subzero temperature accurately.

Fenretinide inhibits the proliferation and migration of human liver cancer HepG2 cells by downregulating the activation of myosin light chain kinase through the p38‑MAPK signaling pathway.

N-(4-hydroxyphenyl)retinamide (4-HPR or fenretinide), which is a synthetic analog of all‑trans retinoic acid (ATRA), effectively inhibits the growth of several types of tumor cells; however, its molecular mechanism remains unclear. We found that 4‑HPR altered the morphology of human liver cancer HepG2 cells and also inhibited their proliferation and suppressed the colony formation in a dose‑ and time‑dependent manner. A wound healing assay revealed that 4‑HPR significantly hindered HepG2 cell migration, and that this was accompanied by the phosphorylation of p38‑MAPK (mitogen‑activated protein kinase).

Hybrid Method Based on Information Gain and Support Vector Machine for Gene Selection in Cancer Classification.

It remains a great challenge to achieve sufficient cancer classification accuracy with the entire set of genes, due to the high dimensions, small sample size, and big noise of gene expression data. We thus proposed a hybrid gene selection method, Information Gain-Support Vector Machine (IG-SVM) in this study. IG was initially employed to filter irrelevant and redundant genes.