A lung nodule segmentation model based on the transformer with multiple thresholds and coordinate attention.

Accurate lung nodule segmentation is fundamental for the early detection of lung cancer. With the rapid development of deep learning, lung nodule segmentation models based on the encoder-decoder structure have become the mainstream research approach. However, during the encoding process, most models have limitations in extracting edge and semantic information and in capturing long-range dependencies.

Comprehensive Review and Comparison for Anticancer Peptides Identification Models.

Anticancer peptides (ACPs) eliminate pathogenic bacteria and kill tumor cells, showing no hemolysis and no damages to normal human cells. This unique ability explores the possibility of ACPs as therapeutic delivery and its potential applications in clinical therapy. Identifying ACPs is one of the most fundamental and central problems in new antitumor drug research.

Ergonomic Assessment of the Mental Workload Confronted by Surgeons during Laparoscopic Surgery.

Although the mental workload confronted by laparoscopic surgeons is rather high, there is presently no reliable, established method for evaluating this workload. In the present study, four evaluation indices of eye movement metrics were applied to evaluate surgeons' mental workload. Correlations between these indices and National Aeronautics and Space Administration Task Load Index (NASA-TLX) scores were also explored.

Inverse planning optimization method for intensity modulated radiation therapy.

In order to facilitate the leaf sequencing process in intensity modulated radiation therapy (IMRT), and design of a practical leaf sequencing algorithm, it is an important issue to smooth the planned fluence maps. The objective is to achieve both high-efficiency and high-precision dose delivering by considering characteristics of leaf sequencing process. The key factor which affects total number of monitor units for the leaf sequencing optimization process is the max flow value of the digraph which formulated from the fluence maps.

Fluence map optimization (FMO) with dose-volume constraints in IMRT using the geometric distance sorting method.

A new heuristic algorithm based on the so-called geometric distance sorting technique is proposed for solving the fluence map optimization with dose-volume constraints which is one of the most essential tasks for inverse planning in IMRT. The framework of the proposed method is basically an iterative process which begins with a simple linear constrained quadratic optimization model without considering any dose-volume constraints, and then the dose constraints for the voxels violating the dose-volume constraints are gradually added into the quadratic optimization model step by step until all the dose-volume constraints are satisfied. In each iteration step, an interior point method is adopted to solve each new linear constrained quadratic programming.

Fissures segmentation using surface features: content-based retrieval for mammographic mass using ensemble classifier.

Rationale And Objectives: Accurate classification is critical in mammography computer-aided diagnosis using content-based image retrieval approaches (CBIR CAD). The objectives of this study were to: 1) develop an accurate ensemble classifier based on domain knowledge and a robust feature selection method for CBIR CAD; 2) propose three new features; and 3) assess the performance of the proposed method and new features by using a relatively large imaging data set.

Materials And Methods: The data set used in this study consisted of 2114 regions of interest (ROI) extracted from a publicly available image database.

Hybrid segmentation of mass in mammograms using template matching and dynamic programming.

Rationale And Objectives: Accurate image segmentation for breast lesions is a critical step in computer-aided diagnosis systems. The objective of this study was to develop a robust method for the automatic segmentation of breast masses on mammograms to extract feasible features for computer-aided diagnosis systems.

Materials And Methods: The data set used in this study consisted of 483 regions of interest extracted from 328 patients.