Diagnostic Biomedical Signal and Image Processing Applications With Deep Learning Methods
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Diagnostic Biomedical Signal and Image Processing Applications With Deep Learning Methods
With Deep Learning Methods
Polat, Kemal; OEztuerk, Saban
Elsevier Science & Technology
05/2023
302
Mole
Inglês
9780323961295
15 a 20 dias
Descrição não disponível.
1. Introduction to Deep Learning and Diagnosis in Medicine
2. 1D CNN based identification of Sleep disorders using EEG signals
3. Classification of Histopathological Colon Cancer Images Using PSO based Feature Selection Algorithm
4. Arrhythmia Diagnosis from ECG Signal Pulses with One?Dimensional Convolutional Neural Network
5. Patch-based Approaches to Whole Slide Histologic Grading of Breast Cancer using Convolutional Neural Networks
6. Deep neural architecture for the breast cancer detection from medical CT image modalities
7. Automated Analysis of Phase-Contrast Optical Microscopy Time-Lapse Images: Application to Wound Healing and Cell Motility Assays of Breast Cancer
8. Automatic detection of normal structures and pathological changes in radiological chest images using deep learning methods
9. Adversarial attacks: dependence on medical image type, CNN architecture as well as on the attack and defense methods
10. A Deep Ensemble Network for Lung Segmentation with Stochastic Weighted Averaging
11. Ensemble of segmentation approaches based on convolutional neural networks
12. Classification of diseases from CT images using LSTM based CNN This chapter explains LSTM modules, CT dataset, and CT related diseases
13. A Novel Polyp Segmentation Approach using U-net with Saliency-like Feature Fusion
2. 1D CNN based identification of Sleep disorders using EEG signals
3. Classification of Histopathological Colon Cancer Images Using PSO based Feature Selection Algorithm
4. Arrhythmia Diagnosis from ECG Signal Pulses with One?Dimensional Convolutional Neural Network
5. Patch-based Approaches to Whole Slide Histologic Grading of Breast Cancer using Convolutional Neural Networks
6. Deep neural architecture for the breast cancer detection from medical CT image modalities
7. Automated Analysis of Phase-Contrast Optical Microscopy Time-Lapse Images: Application to Wound Healing and Cell Motility Assays of Breast Cancer
8. Automatic detection of normal structures and pathological changes in radiological chest images using deep learning methods
9. Adversarial attacks: dependence on medical image type, CNN architecture as well as on the attack and defense methods
10. A Deep Ensemble Network for Lung Segmentation with Stochastic Weighted Averaging
11. Ensemble of segmentation approaches based on convolutional neural networks
12. Classification of diseases from CT images using LSTM based CNN This chapter explains LSTM modules, CT dataset, and CT related diseases
13. A Novel Polyp Segmentation Approach using U-net with Saliency-like Feature Fusion
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
AI; Adversarial attacks; Arrhythmia classification; Artificial intelligence; Biomedical images; Breast cancer; CNN with LSTM; Cardiovascular disease; Cell motility; Chest X-ray; Chest X-rays (CXR); Classification; Colon cancer; Colonoscopy; Convolutional neural network; Convolutional neural network (CNN); Convolutional neural networks; Deep convolutional neural network; Deep learning; Defense methods; Detection; ECG signal beats; Electroencephalography (EEG); Ensembles; Explainable AI; Extrapulmonary pathologies; Histologic grade; Image classification; Image processing; Lung segmentation; Machine learning; Medical diagnosis; Medicine; Neural network; One dimension CNN; Pathology; Phase-contrast optical microscopy; Polyp detection; Polysomnogram; Pre-processing; Pulmonary pathologies; Quantification; Segmentation; Sleep disorder; Time series CNN; Tracking; Transfer learning; U-Net; Wavelet; Web services; Workflow; Wound healing
1. Introduction to Deep Learning and Diagnosis in Medicine
2. 1D CNN based identification of Sleep disorders using EEG signals
3. Classification of Histopathological Colon Cancer Images Using PSO based Feature Selection Algorithm
4. Arrhythmia Diagnosis from ECG Signal Pulses with One?Dimensional Convolutional Neural Network
5. Patch-based Approaches to Whole Slide Histologic Grading of Breast Cancer using Convolutional Neural Networks
6. Deep neural architecture for the breast cancer detection from medical CT image modalities
7. Automated Analysis of Phase-Contrast Optical Microscopy Time-Lapse Images: Application to Wound Healing and Cell Motility Assays of Breast Cancer
8. Automatic detection of normal structures and pathological changes in radiological chest images using deep learning methods
9. Adversarial attacks: dependence on medical image type, CNN architecture as well as on the attack and defense methods
10. A Deep Ensemble Network for Lung Segmentation with Stochastic Weighted Averaging
11. Ensemble of segmentation approaches based on convolutional neural networks
12. Classification of diseases from CT images using LSTM based CNN This chapter explains LSTM modules, CT dataset, and CT related diseases
13. A Novel Polyp Segmentation Approach using U-net with Saliency-like Feature Fusion
2. 1D CNN based identification of Sleep disorders using EEG signals
3. Classification of Histopathological Colon Cancer Images Using PSO based Feature Selection Algorithm
4. Arrhythmia Diagnosis from ECG Signal Pulses with One?Dimensional Convolutional Neural Network
5. Patch-based Approaches to Whole Slide Histologic Grading of Breast Cancer using Convolutional Neural Networks
6. Deep neural architecture for the breast cancer detection from medical CT image modalities
7. Automated Analysis of Phase-Contrast Optical Microscopy Time-Lapse Images: Application to Wound Healing and Cell Motility Assays of Breast Cancer
8. Automatic detection of normal structures and pathological changes in radiological chest images using deep learning methods
9. Adversarial attacks: dependence on medical image type, CNN architecture as well as on the attack and defense methods
10. A Deep Ensemble Network for Lung Segmentation with Stochastic Weighted Averaging
11. Ensemble of segmentation approaches based on convolutional neural networks
12. Classification of diseases from CT images using LSTM based CNN This chapter explains LSTM modules, CT dataset, and CT related diseases
13. A Novel Polyp Segmentation Approach using U-net with Saliency-like Feature Fusion
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
AI; Adversarial attacks; Arrhythmia classification; Artificial intelligence; Biomedical images; Breast cancer; CNN with LSTM; Cardiovascular disease; Cell motility; Chest X-ray; Chest X-rays (CXR); Classification; Colon cancer; Colonoscopy; Convolutional neural network; Convolutional neural network (CNN); Convolutional neural networks; Deep convolutional neural network; Deep learning; Defense methods; Detection; ECG signal beats; Electroencephalography (EEG); Ensembles; Explainable AI; Extrapulmonary pathologies; Histologic grade; Image classification; Image processing; Lung segmentation; Machine learning; Medical diagnosis; Medicine; Neural network; One dimension CNN; Pathology; Phase-contrast optical microscopy; Polyp detection; Polysomnogram; Pre-processing; Pulmonary pathologies; Quantification; Segmentation; Sleep disorder; Time series CNN; Tracking; Transfer learning; U-Net; Wavelet; Web services; Workflow; Wound healing
