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Biblioteca

MARC Lista (tabellare)
ACM SIGBIO newsletter
ACM SIGBIO newsletter
Pubbl/distr/stampa [New York, N.Y.], : AMC Press, -2001
Disciplina 610.285
Soggetto topico Medicine - Data processing
Biomedical engineering - Data processing
Electronic Data Processing
Medicine
Génie biomédical - Informatique
Médecine - Informatique
Informatique
Soggetto genere / forma Periodical
Periodicals.
ISSN 1557-9506
Formato Materiale a stampa
Livello bibliografico Periodico
Lingua di pubblicazione eng
Altri titoli varianti SIGBIO newsletter
Record Nr. UNINA-9910375865503321
[New York, N.Y.], : AMC Press, -2001
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
ACM SIGBIO newsletter
ACM SIGBIO newsletter
Pubbl/distr/stampa [New York, N.Y.], : AMC Press, -2001
Disciplina 610.285
Soggetto topico Medicine - Data processing
Biomedical engineering - Data processing
Electronic Data Processing
Medicine
Génie biomédical - Informatique
Médecine - Informatique
Informatique
Soggetto genere / forma Periodical
Periodicals.
ISSN 1557-9506
Formato Materiale a stampa
Livello bibliografico Periodico
Lingua di pubblicazione eng
Altri titoli varianti SIGBIO newsletter
Record Nr. UNISA-996201007703316
[New York, N.Y.], : AMC Press, -2001
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Advanced Biomedical Engineering / / Gaetano D. Gargiulo, Alistair McEwan, editors
Advanced Biomedical Engineering / / Gaetano D. Gargiulo, Alistair McEwan, editors
Pubbl/distr/stampa Rijeka, Croatia : , : IntechOpen, , [2011]
Descrizione fisica 1 online resource (vi, 280 pages) : illustrations
Disciplina 610.28
Soggetto topico Biomedical engineering - Data processing
ISBN 953-51-4453-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910138288203321
Rijeka, Croatia : , : IntechOpen, , [2011]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Analyzing the large numbers of variables in biomedical and satellite imagery [[electronic resource] /] / Phillip I. Good
Analyzing the large numbers of variables in biomedical and satellite imagery [[electronic resource] /] / Phillip I. Good
Autore Good Phillip I
Pubbl/distr/stampa Hoboken, N.J., : Wiley, c2011
Descrizione fisica xii, 185 p. : ill
Disciplina 006.3/12
Soggetto topico Data mining
Mathematical statistics
Biomedical engineering - Data processing
Remote sensing - Data processing
Functions of several complex variables
R (Computer program language)
ISBN 1-283-13877-8
0-470-93725-4
9786613138774
0-470-93727-0
1-118-00214-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto ; Machine generated contents note: ; 1. Very Large Arrays -- ; 1.1. Applications -- ; 1.2. Problems -- ; 1.3. Solutions -- ; 2. Permutation Tests -- ; 2.1. Two-Sample Comparison -- ; 2.1.1. Blocks -- ; 2.2. k-Sample Comparison -- ; 2.3. Computing The p-Value -- ; 2.3.1. Monte Carlo Method -- ; 2.3.2. An R Program -- ; 2.4. Multiple-Variable Comparisons -- ; 2.4.1. Euclidean Distance Matrix Analysis -- ; 2.4.2. Hotelling's T2 -- ; 2.4.3. Mantel's U -- ; 2.4.4. Combining Univariate Tests -- ; 2.4.5. Gene Set Enrichment Analysis -- ; 2.5. Categorical Data -- ; 2.6. Software -- ; 2.7. Summary -- ; 3. Applying the Permutation Test -- ; 3.1. Which Variables Should Be Included? -- ; 3.2. Single-Value Test Statistics -- ; 3.2.1. Categorical Data -- ; 3.2.2. A Multivariate Comparison Based on a Summary Statistic -- ; 3.2.3. A Multivariate Comparison Based on Variants of Hotelling's T2
; 3.2.4. Adjusting for Covariates -- ; 3.2.5. Pre-Post Comparisons -- ; 3.2.6. Choosing a Statistic: Time-Course Microarrays -- ; 3.3. Recommended Approaches -- ; 3.4. To Learn More -- ; 4. Biological Background -- ; 4.1. Medical Imaging -- ; 4.1.1. Ultrasound -- ; 4.1.2. EEG/MEG -- ; 4.1.3. Magnetic Resonance Imaging -- ; 4.1.3.1. MRI -- ; 4.1.3.2. fMRI -- ; 4.1.4. Positron Emission Tomography -- ; 4.2. Microarrays -- ; 4.3. To Learn More -- ; 5. Multiple Tests -- ; 5.1. Reducing the Number of Hypotheses to Be Tested -- ; 5.1.1. Normalization -- ; 5.1.2. Selection Methods -- ; 5.1.2.1. Univariate Statistics -- ; 5.1.2.2. Which Statistic? -- ; 5.1.2.3. Heuristic Methods -- ; 5.1.2.4. Which Method? -- ; 5.2. Controlling the Over All Error Rate -- ; 5.2.1. An Example: Analyzing Data from Microarrays -- ; 5.3. Controlling the False Discovery Rate -- ; 5.3.1. An Example: Analyzing Time-Course Data from Microarrays -- ; 5.4. Gene Set Enrichment Analysis
; 5.5. Software for Performing Multiple Simultaneous Tests -- ; 5.5.1. AFNI -- ; 5.5.2. Cyber-T -- ; 5.5.3. dChip -- ; 5.5.4. ExactFDR -- ; 5.5.5. GESS -- ; 5.5.6. HaploView -- ; 5.5.7. MatLab -- ; 5.5.8. R -- ; 5.5.9. SAM -- ; 5.5.10. ParaSam -- ; 5.6. Summary -- ; 5.7. To Learn More -- ; 6. The Bootstrap -- ; 6.1. Samples and Populations -- ; 6.2. Precision of an Estimate -- ; 6.2.1. R Code -- ; 6.2.2. Applying the Bootstrap -- ; 6.2.3. Bootstrap Reproducibility Index -- ; 6.2.4. Estimation in Regression Models -- ; 6.3. Confidence Intervals -- ; 6.3.1. Testing for Equivalence -- ; 6.3.2. Parametric Bootstrap -- ; 6.3.3. Blocked Bootstrap -- ; 6.3.4. Balanced Bootstrap -- ; 6.3.5. Adjusted Bootstrap -- ; 6.3.6. Which Test? -- ; 6.4. Determining Sample Size -- ; 6.4.1. Establish a Threshold -- ; 6.5. Validation -- ; 6.5.1. Cluster Analysis -- ; 6.5.2. Correspondence Analysis -- ; 6.6. Building a Model -- ; 6.7. How Large Should The Samples Be?
; 6.8. Summary -- ; 6.9. To Learn More -- ; 7. Classification Methods -- ; 7.1. Nearest Neighbor Methods -- ; 7.2. Discriminant Analysis -- ; 7.3. Logistic Regression -- ; 7.4. Principal Components -- ; 7.5. Naive Bayes Classifier -- ; 7.6. Heuristic Methods -- ; 7.7. Decision Trees -- ; 7.7.1. A Worked-Through Example -- ; 7.8. Which Algorithm Is Best for Your Application? -- ; 7.8.1. Some Further Comparisons -- ; 7.8.2. Validation Versus Cross-validation -- ; 7.9. Improving Diagnostic Effectiveness -- ; 7.9.1. Boosting -- ; 7.9.2. Ensemble Methods -- ; 7.9.3. Random Forests -- ; 7.10. Software for Decision Trees -- ; 7.11. Summary -- ; 8. Applying Decision Trees -- ; 8.1. Photographs -- ; 8.2. Ultrasound -- ; 8.3. MRI Images -- ; 8.4. EEGs and EMGs -- ; 8.5. Misclassification Costs -- ; 8.6. Receiver Operating Characteristic -- ; 8.7. When the Categories Are As Yet Undefined -- ; 8.7.1. Unsupervised Principal Components Applied to fMRI
; 8.7.2. Supervised Principal Components Applied to Microarrays -- ; 8.8. Ensemble Methods -- ; 8.9. Maximally Diversified Multiple Trees -- ; 8.10. Putting It All Together -- ; 8.11. Summary -- ; 8.12. To Learn More -- Glossary of Biomedical Terminology -- Glossary of Statistical Terminology -- Appendix: An R Primer -- ; R1. Getting Started -- ; R1.1. R Functions -- ; R1.2. Vector Arithmetic -- ; R2. Store and Retrieve Data -- ; R2.1. Storing and Retrieving Files from Within R -- ; R2.2. The Tabular Format -- ; R2.3. Comma Separated Format -- ; R3. Resampling -- ; R3.1. The While Command -- ; R4. Expanding R's Capabilities -- ; R4.1. Downloading Libraries of R Functions -- ; R4.2. Programming Your Own Functions.
Record Nr. UNINA-9910208830603321
Good Phillip I  
Hoboken, N.J., : Wiley, c2011
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Analyzing the large numbers of variables in biomedical and satellite imagery / / Phillip I. Good
Analyzing the large numbers of variables in biomedical and satellite imagery / / Phillip I. Good
Autore Good Phillip I
Edizione [1st ed.]
Pubbl/distr/stampa Hoboken, N.J., : Wiley, c2011
Descrizione fisica xii, 185 p. : ill
Disciplina 006.3/12
Soggetto topico Data mining
Mathematical statistics
Biomedical engineering - Data processing
Remote sensing - Data processing
Functions of several complex variables
R (Computer program language)
ISBN 1-283-13877-8
0-470-93725-4
9786613138774
0-470-93727-0
1-118-00214-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto ; Machine generated contents note: ; 1. Very Large Arrays -- ; 1.1. Applications -- ; 1.2. Problems -- ; 1.3. Solutions -- ; 2. Permutation Tests -- ; 2.1. Two-Sample Comparison -- ; 2.1.1. Blocks -- ; 2.2. k-Sample Comparison -- ; 2.3. Computing The p-Value -- ; 2.3.1. Monte Carlo Method -- ; 2.3.2. An R Program -- ; 2.4. Multiple-Variable Comparisons -- ; 2.4.1. Euclidean Distance Matrix Analysis -- ; 2.4.2. Hotelling's T2 -- ; 2.4.3. Mantel's U -- ; 2.4.4. Combining Univariate Tests -- ; 2.4.5. Gene Set Enrichment Analysis -- ; 2.5. Categorical Data -- ; 2.6. Software -- ; 2.7. Summary -- ; 3. Applying the Permutation Test -- ; 3.1. Which Variables Should Be Included? -- ; 3.2. Single-Value Test Statistics -- ; 3.2.1. Categorical Data -- ; 3.2.2. A Multivariate Comparison Based on a Summary Statistic -- ; 3.2.3. A Multivariate Comparison Based on Variants of Hotelling's T2
; 3.2.4. Adjusting for Covariates -- ; 3.2.5. Pre-Post Comparisons -- ; 3.2.6. Choosing a Statistic: Time-Course Microarrays -- ; 3.3. Recommended Approaches -- ; 3.4. To Learn More -- ; 4. Biological Background -- ; 4.1. Medical Imaging -- ; 4.1.1. Ultrasound -- ; 4.1.2. EEG/MEG -- ; 4.1.3. Magnetic Resonance Imaging -- ; 4.1.3.1. MRI -- ; 4.1.3.2. fMRI -- ; 4.1.4. Positron Emission Tomography -- ; 4.2. Microarrays -- ; 4.3. To Learn More -- ; 5. Multiple Tests -- ; 5.1. Reducing the Number of Hypotheses to Be Tested -- ; 5.1.1. Normalization -- ; 5.1.2. Selection Methods -- ; 5.1.2.1. Univariate Statistics -- ; 5.1.2.2. Which Statistic? -- ; 5.1.2.3. Heuristic Methods -- ; 5.1.2.4. Which Method? -- ; 5.2. Controlling the Over All Error Rate -- ; 5.2.1. An Example: Analyzing Data from Microarrays -- ; 5.3. Controlling the False Discovery Rate -- ; 5.3.1. An Example: Analyzing Time-Course Data from Microarrays -- ; 5.4. Gene Set Enrichment Analysis
; 5.5. Software for Performing Multiple Simultaneous Tests -- ; 5.5.1. AFNI -- ; 5.5.2. Cyber-T -- ; 5.5.3. dChip -- ; 5.5.4. ExactFDR -- ; 5.5.5. GESS -- ; 5.5.6. HaploView -- ; 5.5.7. MatLab -- ; 5.5.8. R -- ; 5.5.9. SAM -- ; 5.5.10. ParaSam -- ; 5.6. Summary -- ; 5.7. To Learn More -- ; 6. The Bootstrap -- ; 6.1. Samples and Populations -- ; 6.2. Precision of an Estimate -- ; 6.2.1. R Code -- ; 6.2.2. Applying the Bootstrap -- ; 6.2.3. Bootstrap Reproducibility Index -- ; 6.2.4. Estimation in Regression Models -- ; 6.3. Confidence Intervals -- ; 6.3.1. Testing for Equivalence -- ; 6.3.2. Parametric Bootstrap -- ; 6.3.3. Blocked Bootstrap -- ; 6.3.4. Balanced Bootstrap -- ; 6.3.5. Adjusted Bootstrap -- ; 6.3.6. Which Test? -- ; 6.4. Determining Sample Size -- ; 6.4.1. Establish a Threshold -- ; 6.5. Validation -- ; 6.5.1. Cluster Analysis -- ; 6.5.2. Correspondence Analysis -- ; 6.6. Building a Model -- ; 6.7. How Large Should The Samples Be?
; 6.8. Summary -- ; 6.9. To Learn More -- ; 7. Classification Methods -- ; 7.1. Nearest Neighbor Methods -- ; 7.2. Discriminant Analysis -- ; 7.3. Logistic Regression -- ; 7.4. Principal Components -- ; 7.5. Naive Bayes Classifier -- ; 7.6. Heuristic Methods -- ; 7.7. Decision Trees -- ; 7.7.1. A Worked-Through Example -- ; 7.8. Which Algorithm Is Best for Your Application? -- ; 7.8.1. Some Further Comparisons -- ; 7.8.2. Validation Versus Cross-validation -- ; 7.9. Improving Diagnostic Effectiveness -- ; 7.9.1. Boosting -- ; 7.9.2. Ensemble Methods -- ; 7.9.3. Random Forests -- ; 7.10. Software for Decision Trees -- ; 7.11. Summary -- ; 8. Applying Decision Trees -- ; 8.1. Photographs -- ; 8.2. Ultrasound -- ; 8.3. MRI Images -- ; 8.4. EEGs and EMGs -- ; 8.5. Misclassification Costs -- ; 8.6. Receiver Operating Characteristic -- ; 8.7. When the Categories Are As Yet Undefined -- ; 8.7.1. Unsupervised Principal Components Applied to fMRI
; 8.7.2. Supervised Principal Components Applied to Microarrays -- ; 8.8. Ensemble Methods -- ; 8.9. Maximally Diversified Multiple Trees -- ; 8.10. Putting It All Together -- ; 8.11. Summary -- ; 8.12. To Learn More -- Glossary of Biomedical Terminology -- Glossary of Statistical Terminology -- Appendix: An R Primer -- ; R1. Getting Started -- ; R1.1. R Functions -- ; R1.2. Vector Arithmetic -- ; R2. Store and Retrieve Data -- ; R2.1. Storing and Retrieving Files from Within R -- ; R2.2. The Tabular Format -- ; R2.3. Comma Separated Format -- ; R3. Resampling -- ; R3.1. The While Command -- ; R4. Expanding R's Capabilities -- ; R4.1. Downloading Libraries of R Functions -- ; R4.2. Programming Your Own Functions.
Record Nr. UNINA-9910825098303321
Good Phillip I  
Hoboken, N.J., : Wiley, c2011
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Biomedical data analysis and processing using explainable (XAI) and responsive artificial intelligence (RAI) / / Aditya Khamparia [and three others]
Biomedical data analysis and processing using explainable (XAI) and responsive artificial intelligence (RAI) / / Aditya Khamparia [and three others]
Pubbl/distr/stampa Singapore : , : Springer, , [2022]
Descrizione fisica 1 online resource (148 pages)
Disciplina 610.28
Collana Intelligent Systems Reference Library
Soggetto topico Biomedical engineering - Data processing
Artificial intelligence - Data processing
ISBN 981-19-1476-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- Editors and Contributors -- 1 Optimal Boosting Label Weighting Extreme Learning Machine for Mental Disorder Prediction and Classification -- 1.1 Introduction -- 1.2 The Proposed Model -- 1.2.1 Data Pre-processing -- 1.2.2 Process Involved in BWELM Model -- 1.2.3 Parameter Tuning Using CSPSO Algorithm -- 1.3 Experimental Validation -- 1.4 Conclusion -- References -- 2 Modeling of Explainable Artificial Intelligence with Correlation-Based Feature Selection Approach for Biomedical Data Analysis -- 2.1 Introduction -- 2.2 The Proposed Model -- 2.2.1 Stage 1: Pre-processing -- 2.2.2 Stage 2: Correlation-Based Feature Selection -- 2.2.3 Stage 3: FKNN-Based Classification -- 2.2.4 Stage 4: BWO-Based Classification -- 2.3 Experimental Validation -- 2.4 Conclusion -- References -- 3 Explainable Machine Learning Model for Diagnosis of Parkinson Disorder -- 3.1 Introduction -- 3.2 Literature Survey -- 3.3 Experimental Work -- 3.4 Discussion -- 3.5 Conclusion -- References -- 4 Explainable Artificial Intelligence with Metaheuristic Feature Selection Technique for Biomedical Data Classification -- 4.1 Introduction -- 4.2 Literature Review -- 4.3 The Proposed Model -- 4.3.1 Data Preprocessing -- 4.3.2 Algorithmic Design of CSMO Based Feature Selection -- 4.3.3 Process Involved in Optimal DNN-Based Classification -- 4.4 Experimental Validation -- 4.5 Conclusion -- References -- 5 Explainable AI in Neural Networks Using Shapley Values -- 5.1 Introduction -- 5.2 Literature Review -- 5.2.1 Explanation by Simplification -- 5.2.2 Explanation by Feature Attribution -- 5.3 Shapley Values and Game Theory -- 5.3.1 Using Shapley Values to Attributing Relevance -- 5.3.2 Shapley Value to SHAP -- 5.4 Explainer Architecture -- 5.4.1 Model Explainer -- 5.4.2 Visualization -- 5.4.3 Model Refinement -- 5.4.4 Reporting and Presentation.
5.5 Discussion -- 5.5.1 Comparison with Other Explainable Methods -- 5.5.2 Axiomatic Comparison -- 5.6 Conclusion and Future Work -- References -- 6 Design of Multimodal Fusion-Based Deep Learning Approach for COVID-19 Diagnosis Using Chest X-Ray Images -- 6.1 Introduction -- 6.2 Literature Survey -- 6.3 The Proposed MMFBDL Model -- 6.3.1 Feature Extraction Process -- 6.3.2 Image Classification Using MLP -- 6.4 Experimental Validation -- 6.5 Conclusion -- References -- 7 ECG Classification and Analysis for Heart Disease Prediction Using XAI-Driven Machine Learning Algorithms -- 7.1 Introduction -- 7.2 Literature Review -- 7.3 Dataset Methods and Classification -- 7.3.1 Tools and Techniques -- 7.3.2 ECG Results Implementation for Normal and Abnormal -- 7.3.3 Results for Individual Disease by Cross-Validation Score -- 7.3.4 Cross-Validation Score for ANN -- 7.4 Description of the ML Models -- 7.4.1 Logistic Regression -- 7.4.2 Naive Bayes -- 7.4.3 Decision Trees -- 7.4.4 Support Vector Machine (SVM) -- 7.4.5 Lime -- 7.4.6 DeepLIFT -- 7.4.7 Skater -- 7.4.8 Shapley -- 7.5 Results Analysis -- 7.6 Conclusion and Future Scope -- References -- 8 Rethinking the Transfer Learning Architecture for Respiratory Diseases and COVID-19 Diagnosis -- 8.1 Introduction -- 8.2 Literature Reviews -- 8.3 Description of Dataset -- 8.4 Methodology -- 8.4.1 VGG-16 -- 8.4.2 XceptionNet Model -- 8.5 Result Analysis -- 8.6 Conclusion -- References -- 9 Arithmetic Optimization Algorithm with Explainable Artificial Intelligence Technique for Biomedical Signal Analysis -- 9.1 Introduction -- 9.2 Related Works -- 9.3 The Proposed Model -- 9.3.1 Variation Mode Decomposition (VMD) Approach -- 9.3.2 Feature Extraction Using Bi-LSTM Model -- 9.3.3 ECG Recognition Using Optimal SVM Model -- 9.4 Experimental Validation -- 9.5 Conclusion -- References.
Record Nr. UNINA-9910559392203321
Singapore : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Biomedical data mining for information retrieval : methodologies, techniques, and applications / / edited by Sujata Dash [and three others]
Biomedical data mining for information retrieval : methodologies, techniques, and applications / / edited by Sujata Dash [and three others]
Pubbl/distr/stampa Hoboken, New Jersey ; ; Beverly, Massachusetts : , : Wiley : , : Scrivener Publishing, , [2021]
Descrizione fisica 1 online resource (448 pages)
Disciplina 610.28
Collana Artificial intelligence and soft computing for industrial transformation
Soggetto topico Biomedical engineering - Data processing
Soggetto genere / forma Electronic books.
ISBN 1-119-71126-6
1-119-71127-4
1-119-71125-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910554833903321
Hoboken, New Jersey ; ; Beverly, Massachusetts : , : Wiley : , : Scrivener Publishing, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Biomedical data mining for information retrieval : methodologies, techniques, and applications / / edited by Sujata Dash [and three others]
Biomedical data mining for information retrieval : methodologies, techniques, and applications / / edited by Sujata Dash [and three others]
Pubbl/distr/stampa Hoboken, New Jersey ; ; Beverly, Massachusetts : , : Wiley : , : Scrivener Publishing, , [2021]
Descrizione fisica 1 online resource (448 pages)
Disciplina 610.28
Collana Artificial intelligence and soft computing for industrial transformation
Soggetto topico Biomedical engineering - Data processing
ISBN 1-119-71126-6
1-119-71127-4
1-119-71125-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910677724303321
Hoboken, New Jersey ; ; Beverly, Massachusetts : , : Wiley : , : Scrivener Publishing, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Biomedical data mining for information retrieval : methodologies, techniques, and applications / / edited by Sujata Dash [and three others]
Biomedical data mining for information retrieval : methodologies, techniques, and applications / / edited by Sujata Dash [and three others]
Pubbl/distr/stampa Hoboken, New Jersey ; ; Beverly, Massachusetts : , : Wiley : , : Scrivener Publishing, , [2021]
Descrizione fisica 1 online resource (448 pages)
Disciplina 610.28
Collana Artificial intelligence and soft computing for industrial transformation
Soggetto topico Biomedical engineering - Data processing
ISBN 1-119-71126-6
1-119-71127-4
1-119-71125-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910823851603321
Hoboken, New Jersey ; ; Beverly, Massachusetts : , : Wiley : , : Scrivener Publishing, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Biomedical image understanding : methods and applications / / Joo Hwee Lim, Sim Heng Ong, Wei Xiong
Biomedical image understanding : methods and applications / / Joo Hwee Lim, Sim Heng Ong, Wei Xiong
Autore Lim Joo Hwee
Pubbl/distr/stampa Hoboken, New Jersey : , : John Wiley & Sons, Inc., , 2015
Descrizione fisica 1 online resource (512 p.)
Disciplina 610.28/4
Collana Wiley Series in Biomedical Engineering and Multi-Disciplinary Integrated Systems
Soggetto topico Biomedical engineering - Materials
Biomedical engineering - Data processing
ISBN 1-118-95757-1
1-118-71532-2
1-118-71516-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Overview of biomedical image understanding methods / Wei Xiong, Jierong Cheng, Ying Gu, Shimiao Li and Joo Hwee Lim -- Medical image segmentation and its application in cardiac MRI / Dong Wei, Chao Li and Ying Sun -- Retinal vascular measurements with VAMPIRE / Emanuele Trucco, Andrea Giachetti, Lucia Ballerini, Devanjali Relan, Alessandro Cavinato and Tom MacGillivray -- Analyzing cell and tissue morphologies using pattern recognition algorithms / Hwee Kuan Lee, Yan Nei Law, ChaoHui Huang and Choon Kong Yap -- 3D nonrigid image registration by Parzenwindow based normalized mutual information / Rui Xu, YenWei Chen, Shigehiro Morikawa and Yoshimasa Kurumi -- 2D/3D image registration for endovascular abdominal aortic aneurysm (AAA) repair / Shun Miao and Rui Liao -- Motion tracking in medical images / Chuqing Cao, Chao Li and Ying Sun -- Blood smear analysis and malaria infection detection from blood cell images / Wei Xiong, SimHeng Ong, Joo Hwee Lim, Jierong Cheng and Ying Gu -- Liver tumor segmentation using SVM framework and pathology characterization / Jiayin Zhou, Yanling Chi, Weimin Huang, Wei Xiong, Wenyu Chen, Jimin Liu and Sudhakar K. Venkatesh -- Benchmarking lymph node metastasis classification for gastric cancer staging / Su Zhang, Chao Li, Shuheng Zhang, Lifang Pang and Huan Zhang -- The use of knowledge in biomedical image analysis / Florence Cloppet -- Active shape model for contour detection of anatomical structure / Huiqi Li and Qing Nie.
Record Nr. UNINA-9910140475003321
Lim Joo Hwee  
Hoboken, New Jersey : , : John Wiley & Sons, Inc., , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui