Full dimensional dynamic 3D convolution and point cloud in pulmonary nodule detection.

Lung cancer is a leading cause of death worldwide, making early and accurate diagnosis essential for improving patient outcomes. Recently, deep learning (DL) has proven to be a powerful tool, significantly enhancing the accuracy of computer-aided pulmonary nodule detection (PND). In this study, we introduce a novel approach called the Omni-dimension Dynamic Residual 3D Net (ODR3DNet) for PND, which utilizes full-dimensional dynamic 3D convolution, along with a specialized machine learning algorithm for detecting lung nodules in 3D point clouds.

LRT-CLUSTER: A New Clustering Algorithm Based on Likelihood Ratio Test to Identify Driving Genes.

Somatic mutations often occur at high relapse sites in protein sequences, which indicates that the location clustering of somatic missense mutations can be used to identify driving genes. However, the traditional clustering algorithm has such problems as the background signal over-fitting, the clustering algorithm is not suitable for mutation data, and the performance of identifying low-frequency mutation genes needs to be improved. In this paper, we propose a linear clustering algorithm based on likelihood ratio test knowledge to identify driver genes.

Res-trans networks for lung nodule classification.

Purpose: Lung cancer usually presents as pulmonary nodules on early diagnostic images, and accurately estimating the malignancy of pulmonary nodules is crucial to the prevention and diagnosis of lung cancer. Recently, deep learning algorithms based on convolutional neural networks have shown potential for pulmonary nodules classification. However, the size of the nodules is very diverse, ranging from 3 to 30 mm, which makes classifying them to be a challenging task.

Deep Local Feature Descriptor Learning with Dual Hard Batch Construction.

Local feature descriptor learning aims to represent distinctive images or patches with the same local features, where their representation is invariant under different types of deformation. Recent studies have demonstrated that descriptor learning based on Convolutional Neural Network (CNN) is able to improve the matching performance significantly. However, they tend to ignore the importance of sample selection during the training process, leading to unstable quality of descriptors and learning efficiency.