2008 Special Issue: Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance

Bend resistant multimode optical fibers are disclosed herein. Multimode optical fibers disclosed herein comprise a core region and a cladding region surrounding and directly adjacent to the core region, the cladding region comprising a depressed-index annular portion comprising a depressed relative refractive index.

Bend resistant multimode optical fiber

Various systems and methods are provided for surgical and interventional planning, support, post-operative follow-up, and functional recovery tracking. In general, a patient can be tracked throughout medical treatment including through initial onset of symptoms, diagnosis, non-surgical treatment, surgical treatment, and recovery from the surgical treatment. In one embodiment, a patient and one or more medical professionals involved with treating the patient can electronically access a comprehensive treatment planning, support, and review system. The system can provide recommendations regarding diagnosis, non-surgical treatment, surgical treatment, and recovery from the surgical treatment based on data gathered from the patient and the medical professional(s). The system can manage the tracking of multiple patients, thereby allowing for data comparison between similar aspects of medical treatments and for learning over time through continual data gathering, analysis, and assimilation to decision-making algorithms.

Systems And Methods For Surgical And Interventional Planning, Support, Post-Operative Follow-Up, And, Functional Recovery Tracking

Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms

This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems

https://downloads.hindawi.com/journals/ijbi/2013/942353.pdf

Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies

Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classifier ensemble construction

We propose a feature subset selection method based on genetic algorithms to improve the performance of false positive reduction in lung nodule computer-aided detection (CAD). It is coupled with a classifier based on support vector machines. The proposed approach determines automatically the optimal size of the feature set, and chooses the most relevant features from a feature pool. Its performance was tested using a lung nodule database (52 true nodules and 443 false ones) acquired by multislice CT scans. From 23 features calculated for each detected structure, the suggested method determined ten to be the optimal feature subset size, and selected the most relevant ten features. A support vector machine classifier trained with the optimal feature subset resulted in 100% sensitivity and 56.4% specificity using an independent validation set. Experiments show significant improvement achieved by a system incorporating the proposed method over a system without it. This approach can be also applied to other machine learning problems; e.g. computer-aided diagnosis of lung nodules

Feature Subset Selection for Improving the Performance of False Positive Reduction in Lung Nodule CAD

In this paper, we propose a feature subset selection method based on genetic algorithms to improve the performance of false positive reduction in lung nodule CAD. It is coupled with a classifier based on support vector machines. The proposed approach determines automatically the optimal size of the feature set and chooses the most relevant features from a feature pool. Its performance was tested using a lung nodule database (66 true nodules and 123 false ones) acquired by multi-slice CT scans. From 23 features calculated for each detected structure, the suggested method determined 9 as the optimal feature subset size and selected the nine features. A support vector machine-based classifier trained with the optimal feature subset has resulted in 92.4% sensitivity and 85.4% specificity using leave-one-out cross validation. Experiments also showed significant improvement achieved by a system incorporating the proposed method over a system without it. It can be also applied to other machine learning problems: e.g. computer-aided diagnosis of lung nodules.

Feature subset selection for improving the performance of false positive reduction in lung nodule CAD

Methods are herein provided for decision support in diagnosis of a disease in a subject, and for extracting features from a multi-slice data set. Systems for computer- aided diagnosis are provided. The systems take as input a plurality of medical data and produces as output a diagnosis based upon this data. The inputs may consist of a combination of image data and clinical data. Diagnosis is performed through feature selection and the use of one or more classifier algorithms.

Advanced computer-aided diagnosis of lung nodules

A clinical decision support (CDS) system comprises: a case grouping sub-system (10) including a graphical user interface (30) that is operative to simultaneously display data representing a plurality of patient cases and further configured to enable a user to group selected patient cases represented by the simultaneously displayed data into clinically related groups (32) as selected by the user; a probative features determination sub-system (12) that is operative to determine probative features (44) that correlate with the clinically related groups; and a CDS user interface (16) that is operative to receive current patient data relating to a current patient case and to output clinical decision support information based on values of the probative features determined from the received current patient data.

Lilla Boroczky

Papers

Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classifier ensemble construction

Feature Subset Selection for Improving the Performance of False Positive Reduction in Lung Nodule CAD

Feature subset selection for improving the performance of false positive reduction in lung nodule CAD

Advanced computer-aided diagnosis of lung nodules

Clinical decision support systems and methods