Combining Binary Classifiers for Automatic Cartilage Segmentation in Knee MRI

Next-generation 5-min Knee MRI with Combined Simultaneous Multislice and Parallel Imaging Acceleration: Ready for Prime Time?

10.1055/s-0039-1692573 ◽

2019 ◽

Author(s):

F. Del Grande ◽

A. Rashidi ◽

M. Tanaka ◽

J. Fritz

Keyword(s):

Parallel Imaging ◽

Prime Time ◽

Next Generation ◽

Knee Mri ◽

Simultaneous Multislice ◽

Parallel Imaging Acceleration

Download Full-text

Sky Segmentation for Enhanced Depth Reconstruction and Bokeh Rendering with Efficient Architectures

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.14.coimg-378 ◽

2020 ◽

Vol 2020 (14) ◽

pp. 378-1-378-7

Author(s):

Tyler Nuanes ◽

Matt Elsey ◽

Radek Grzeszczuk ◽

John Paul Shen

Keyword(s):

Real Time ◽

Mobile Device ◽

Computational Cost ◽

False Positives ◽

Compact Model ◽

High Quality ◽

False Negatives ◽

Trade Off ◽

Depth Reconstruction ◽

Binary Classifiers

We present a high-quality sky segmentation model for depth refinement and investigate residual architecture performance to inform optimally shrinking the network. We describe a model that runs in near real-time on mobile device, present a new, highquality dataset, and detail a unique weighing to trade off false positives and false negatives in binary classifiers. We show how the optimizations improve bokeh rendering by correcting stereo depth misprediction in sky regions. We detail techniques used to preserve edges, reject false positives, and ensure generalization to the diversity of sky scenes. Finally, we present a compact model and compare performance of four popular residual architectures (ShuffleNet, MobileNetV2, Resnet-101, and Resnet-34-like) at constant computational cost.

Download Full-text

Faculty Opinions recommendation of The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.725375858.793530299 ◽

2017 ◽

Author(s):

Michael Barnes ◽

David Watson

Keyword(s):

Imbalanced Datasets ◽

Binary Classifiers

Download Full-text

Hybrid Design of Isonicotinic Acid Hydrazide Derivatives: Machine Learning Studies, Synthesis and Biological Evaluation of their Antituberculosis Activity

Current Drug Discovery Technologies ◽

10.2174/1570163816666190411110331 ◽

2020 ◽

Vol 17 (3) ◽

pp. 365-375

Author(s):

Vasyl Kovalishyn ◽

Diana Hodyna ◽

Vitaliy O. Sinenko ◽

Volodymyr Blagodatny ◽

Ivan Semenyuta ◽

...

Keyword(s):

Machine Learning ◽

Isonicotinic Acid ◽

Acid Hydrazide ◽

Predictive Ability ◽

Antitubercular Activity ◽

Biological Evaluation ◽

Starting Point ◽

Binary Classifiers ◽

Synthesis And Biological Evaluation

Background: Tuberculosis (TB) is an infection disease caused by Mycobacterium tuberculosis (Mtb) bacteria. One of the main causes of mortality from TB is the problem of Mtb resistance to known drugs. Objective: The goal of this work is to identify potent small molecule anti-TB agents by machine learning, synthesis and biological evaluation. Methods: The On-line Chemical Database and Modeling Environment (OCHEM) was used to build predictive machine learning models. Seven compounds were synthesized and tested in vitro for their antitubercular activity against H37Rv and resistant Mtb strains. Results: A set of predictive models was built with OCHEM based on a set of previously synthesized isoniazid (INH) derivatives containing a thiazole core and tested against Mtb. The predictive ability of the models was tested by a 5-fold cross-validation, and resulted in balanced accuracies (BA) of 61–78% for the binary classifiers. Test set validation showed that the models could be instrumental in predicting anti- TB activity with a reasonable accuracy (with BA = 67–79 %) within the applicability domain. Seven designed compounds were synthesized and demonstrated activity against both the H37Rv and multidrugresistant (MDR) Mtb strains resistant to rifampicin and isoniazid. According to the acute toxicity evaluation in Daphnia magna neonates, six compounds were classified as moderately toxic (LD50 in the range of 10−100 mg/L) and one as practically harmless (LD50 in the range of 100−1000 mg/L). Conclusion: The newly identified compounds may represent a starting point for further development of therapies against Mtb. The developed models are available online at OCHEM http://ochem.eu/article/11 1066 and can be used to virtually screen for potential compounds with anti-TB activity.

Download Full-text

Routing Memento Requests Using Binary Classifiers

Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries - JCDL '16 ◽

10.1145/2910896.2910899 ◽

2016 ◽

Cited By ~ 5

Author(s):

Nicolas J. Bornand ◽

Lyudmila Balakireva ◽

Herbert Van de Sompel

Keyword(s):

Binary Classifiers

Download Full-text

Interclass Interference Suppression in Multi-Class Problems

Applied Sciences ◽

10.3390/app11010450 ◽

2021 ◽

Vol 11 (1) ◽

pp. 450

Author(s):

Jinfu Liu ◽

Mingliang Bai ◽

Na Jiang ◽

Ran Cheng ◽

Xianling Li ◽

...

Keyword(s):

Classification Accuracy ◽

Cross Validation ◽

Selection Process ◽

Interference Suppression ◽

Generalization Ability ◽

Suppression Effect ◽

Binary Classifiers ◽

The One ◽

Fold Cross Validation ◽

Validation Experiments

Multi-classifiers are widely applied in many practical problems. But the features that can significantly discriminate a certain class from others are often deleted in the feature selection process of multi-classifiers, which seriously decreases the generalization ability. This paper refers to this phenomenon as interclass interference in multi-class problems and analyzes its reason in detail. Then, this paper summarizes three interclass interference suppression methods including the method based on all-features, one-class classifiers and binary classifiers and compares their effects on interclass interference via the 10-fold cross-validation experiments in 14 UCI datasets. Experiments show that the method based on binary classifiers can suppress the interclass interference efficiently and obtain the best classification accuracy among the three methods. Further experiments were done to compare the suppression effect of two methods based on binary classifiers including the one-versus-one method and one-versus-all method. Results show that the one-versus-one method can obtain a better suppression effect on interclass interference and obtain better classification accuracy. By proposing the concept of interclass inference and studying its suppression methods, this paper significantly improves the generalization ability of multi-classifiers.

Download Full-text

Creation and Validation of a Shorthand Magnetic Resonance Imaging Bone Age Assessment Tool of the Knee as an Alternative Skeletal Maturity Assessment

The American Journal of Sports Medicine ◽

10.1177/03635465211032986 ◽

2021 ◽

pp. 036354652110329

Author(s):

Cary S. Politzer ◽

James D. Bomar ◽

Hakan C. Pehlivan ◽

Pradyumna Gurusamy ◽

Eric W. Edmonds ◽

...

Keyword(s):

Assessment Tool ◽

Skeletal Maturity ◽

Bone Age ◽

Mri Scan ◽

Perfect Agreement ◽

Age Assessment ◽

Left Hand ◽

Knee Mri ◽

Bone Age Assessment ◽

Hand Radiograph

Background: In managing pediatric knee conditions, an accurate bone age assessment is often critical for diagnostic, prognostic, and treatment purposes. Historically, the Greulich and Pyle atlas (hand atlas) has been the gold standard bone age assessment tool. In 2013, a shorthand bone age assessment tool based on this atlas (hand shorthand) was devised as a simpler and more efficient alternative. Recently, a knee magnetic resonance imaging (MRI) bone age atlas (MRI atlas) was created to circumvent the need for a left-hand radiograph. Purpose: To create a shorthand version of the knee MRI atlas. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: A shorthand bone age assessment method was created utilizing the previously published MRI atlas, which utilizes several criteria that are visualized across a series of images. The MRI shorthand draws on characteristic criteria for each age that are best observed on a single MRI scan. For validation, we performed a retrospective assessment of skeletally immature patients. One reader performed the bone age assessment using the MRI atlas and the MRI shorthand on 200 patients. Then, 4 readers performed the bone age assessment with the hand atlas, hand shorthand, MRI atlas, and MRI shorthand on a subset of 22 patients in a blinded fashion. All 22 patients had a knee MRI scan and a left-hand radiograph within 4 weeks of each other. Interobserver and intraobserver reliability, as well as variability among observers, were evaluated. Results: A total of 200 patients with a mean age of 13.5 years (range, 9.08-17.98 years) were included in this study. Also, 22 patients with a mean age of 13.3 years (range, 9.0-15.6 years) had a knee MRI scan and a left-hand radiograph within 4 weeks. The intraobserver and interobserver reliability of all 4 assessment tools were acceptable (intraclass correlation coefficient [ICC] ≥ 0.8; P < .001). When comparing the MRI shorthand with the MRI atlas, there was excellent agreement (ICC = 0.989), whereas the hand shorthand compared with the hand atlas had good agreement (ICC = 0.765). The MRI shorthand also had perfect agreement in 50% of readings among all 4 readers, and 95% of readings had agreement within 1 year, whereas the hand shorthand had perfect agreement in 32% of readings and 77% agreement within 1 year. Conclusion: The MRI shorthand is a simple and efficient means of assessing the skeletal maturity of adolescent patients with a knee MRI scan. This bone age assessment technique had interobserver and intraobserver reliability equivalent to or better than the standard method of utilizing a left-hand radiograph.

Download Full-text