Fine-tuned feature selection to improve prostate segmentation via a fully connected meta-learner architecture-Reference-Cited by-同舟云学术

Fine-tuned feature selection to improve prostate segmentation via a fully connected meta-learner architecture

Published:2022-09-27 Issue: Volume: Page:
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Container-title:2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)
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Author:

Zaridis Dimitris¹,Mylona Eugenia¹,Tachos Nikolaos¹,Marias Kostas²,Tsiknakis Manolis³,Fotiadis Dimitios I.⁴

Affiliation:

1. FORTH-BRI,Dept. of Biomedical Research,Ioannina,Greece

2. Inst. of Computer Science and Electrical Computer Engineering, FORTH,Heraklion,Greece

3. FORTH and Hellenic Mediterranean University,Inst. of Computer Science & Dept. of Electrical Computer Engineering,Heraklion,Greece

4. University of Ioannina,Inst. of Biomedical Research & Unit of Medical Technology and Intelligent Information Systems, FORTH-BRI & Materials Science and Engineering Dept.,Ioannina,Greece

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9926747/9926739/09926929.pdf?arnumber=9926929

Reference18 articles.

1. Sensitivity, Specificity, and Predictive Values: Foundations, Pliabilities, and Pitfalls in Research and Practice

2. The Balanced Accuracy and Its Posterior Distribution

3. Statistical validation of image segmentation quality based on a spatial overlap index1

4. Comparing images using the Hausdorff distance

5. A Measure for Objective Evaluation of Image Segmentation Algorithms

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1. Region-adaptive magnetic resonance image enhancement for improving CNN-based segmentation of the prostate and prostatic zones;Scientific Reports;2023-01-13