PDC-Net: parallel dilated convolutional network with channel attention mechanism for pituitary adenoma segmentation.

Accurate segmentation of the medical image is the basis and premise of intelligent diagnosis and treatment, which has a wide range of clinical application value. However, the robustness and effectiveness of medical image segmentation algorithms remains a challenging subject due to the unbalanced categories, blurred boundaries, highly variable anatomical structures and lack of training samples. For this reason, we present a parallel dilated convolutional network (PDC-Net) to address the pituitary adenoma segmentation in magnetic resonance imaging images.

Modified UNet++ with atrous spatial pyramid pooling for blood cell image segmentation.

Blood cell image segmentation is an important part of the field of computer-aided diagnosis. However, due to the low contrast, large differences in cell morphology and the scarcity of labeled images, the segmentation performance of cells cannot meet the requirements of an actual diagnosis. To address the above limitations, we present a deep learning-based approach to study cell segmentation on pathological images.

[Cloning and expression of fusion gene of transmembrane domain of human CD20 and g3pN in Escherichia coli].

Aim: To construct the expression vector containing transmembrane domain gene of human CD20 and g3pN1 gene and express the fusion gene high-efficiently in E.coli.

Methods: The human CD20 gene and g3pN1 domain gene were amplified by RT-PCR and PCR from Daudi cells and M13K07 phage antibody library, respectively, and then cloned into expression vector pTIG-Trx.