Applications of Medical Artificial Intelligence [[electronic resource] ] : Second International Workshop, AMAI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings / / edited by Shandong Wu, Behrouz Shabestari, Lei Xing

• Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024
• Edizione: 1st ed. 2024.
• Descrizione fisica: 1 online resource (187 pages)
• Disciplina: 610.28563
• Soggetto topico: Computer vision; Application software; Artificial intelligence; Education - Data processing; Social sciences - Data processing; Computer Vision; Computer and Information Systems Applications; Artificial Intelligence; Computers and Education; Computer Application in Social and Behavioral Sciences
• Persona (resp. second.): WuShandong; ShabestariBehrouz; XingLei
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Organization -- Contents -- Clinical Trial Histology Image Based End-to-End Biomarker Expression Levels Prediction and Visualization Using Constrained GANs -- 1 Introduction -- 2 Clinical Trial Data -- 3 Method -- 3.1 Whole Slide Image Registration -- 3.2 Image Tiling and Pre-processing -- 3.3 Activate Learning (AL) for Semantic Segmentation -- 3.4 Biomarker Expression Proportion Score (PS) -- 3.5 Constrained GAN for Virtual IHC Synthesis -- 4 Results and Discussion -- 4.1 Phase 1 Clinical Trial Dataset -- 4.2 Active Learning and PS Score Prediction -- 4.3 Constrained GAN Training and Testing -- 5 Conclusion and Future Work -- References -- More Than Meets the Eye: Physicians' Visual Attention in the Operating Room -- 1 Introduction -- 2 Related Work -- 2.1 Face Detection -- 2.2 Facial Landmarks -- 2.3 Spatiotemporal Gaze Architecture -- 2.4 Eye-Context Interaction Inferring Network -- 3 Materials -- 3.1 Benchmark Datasets -- 3.2 Our Datasets -- 4 Methods -- 4.1 Pipeline Construction -- 4.2 Implementation -- 5 Results -- 5.1 Ablation Study of the End-to-End Pipeline -- 5.2 Evaluation of the Proposed Framework on Our Datasets -- 6 Discussion -- References -- CNNs vs. Transformers: Performance and Robustness in Endoscopic Image Analysis -- 1 Introduction -- 2 Methods -- 2.1 Data: Setting, Datasets and Preprocessing -- 2.2 Network Architectures, Training and Evaluation -- 3 Experimental Results and Discussion -- 4 Conclusions -- References -- Investigating the Impact of Image Quality on Endoscopic AI Model Performance -- 1 Introduction -- 2 Methods and Materials -- 2.1 Network Training -- 2.2 Experiments -- 2.3 Image Corruptions -- 2.4 Evaluation -- 3 Results -- 4 Conclusions and Future Work -- References -- Ensembling Voxel-Based and Box-Based Model Predictions for Robust Lesion Detection -- 1 Introduction. ; 2 Materials and Methods -- 2.1 Imaging Data -- 2.2 Ensembling Method -- 2.3 Evaluation -- 3 Results -- 4 Discussion and Conclusion -- References -- Advancing Abdominal Organ and PDAC Segmentation Accuracy with Task-Specific Interactive Models -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Dataset -- 3.2 Interaction Framework -- 3.3 Training Details and Evaluation Criteria -- 4 Results -- 5 Discussion -- 6 Future Work -- 7 Conclusion -- References -- Anatomical Location-Guided Deep Learning-Based Genetic Cluster Identification of Pheochromocytomas and Paragangliomas from CT Images -- 1 Introduction -- 2 Methods -- 2.1 Dataset -- 2.2 PPGL-Transformer -- 2.3 Supervised Contrastive Learning of PPGL-Transformer -- 2.4 Implementation and Evaluation Details -- 3 Results -- 4 Discussion and Conclusion -- References -- Video-Based Gait Analysis for Assessing Alzheimer's Disease and Dementia with Lewy Bodies -- 1 Introduction and Related Work -- 2 Method -- 2.1 Our Patient Data -- 2.2 MAX-GRNet for 3D Pose Estimation -- 2.3 Geometric Deep Learning for Severity Assessment -- 3 Experiments -- 3.1 Datasets -- 3.2 Implementation Details -- 3.3 Results -- 4 Conclusion -- References -- Enhancing Clinical Support for Breast Cancer with Deep Learning Models Using Synthetic Correlated Diffusion Imaging -- 1 Introduction -- 2 Related Works -- 2.1 Breast Cancer Grading -- 2.2 Pathologic Complete Response Prediction -- 3 Methodology -- 3.1 Patient Cohort and Imaging Protocol -- 3.2 Extracting Deep Radiomic Sequences from CDIs -- 3.3 pCR Prediction via Volumetric Deep Radiomic Features -- 4 Results -- 4.1 Breast Cancer Grading (SBR Grade) -- 4.2 pCR Prediction via Volumetric Deep Radiomic Features -- 5 Conclusion -- References -- Image-Based 3D Reconstruction of Cleft Lip and Palate Using a Learned Shape Prior -- 1 Introduction -- 2 Related Work -- 3 Methods. ; 3.1 Data Pre-processing -- 3.2 Semi-dense Reconstruction -- 3.3 Data-Driven Shape Prior -- 3.4 Data Collection -- 4 Results -- 4.1 3D Reconstruction -- 4.2 Plate Evaluation -- 4.3 Learned Shape Prior -- 5 Conclusion -- References -- Breaking down the Hierarchy: A New Approach to Leukemia Classification -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 3.1 Pre-processing -- 4 Methodology Overview -- 4.1 Flat/Leaf Classification (Baseline) -- 4.2 Hierarchical Multi-label Classification -- 5 Experimental Setup -- 5.1 Model Selection -- 5.2 Simulating Pathological Evaluation -- 5.3 Experimental Procedures -- 5.4 Evaluation Metrics -- 6 Results and Discussion -- 6.1 Flat/Leaf Classification -- 6.2 Base vs Proposed Hierarchical Classification -- 6.3 Flat vs Proposed Hierarchical Classification -- 6.4 Visual Experimental Results -- 7 Conclusion -- References -- Single-Cell Spatial Analysis of Histopathology Images for Survival Prediction via Graph Attention Network -- 1 Introduction -- 2 Method -- 2.1 Nuclei Classification -- 2.2 Cell Graph Construction -- 2.3 Graph Attention Network -- 2.4 Model Interpretability -- 3 Experimental and Results -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Comparison with State-of-the-Art Methods -- 4 Conclusion -- References -- Ultrafast Labeling for Multiplexed Immunobiomarkers from Label-free Fluorescent Images -- 1 Introduction -- 2 Method -- 2.1 Framework Overview -- 2.2 Random MAE Feature Extractor -- 2.3 Dual-Modal Feature Combination With Self-Attention Mechanism -- 2.4 Multitasked Composite Loss Function -- 3 Experiments -- 3.1 Data Preparation -- 3.2 Assessment Scores -- 3.3 Software and Platform -- 4 Results -- 4.1 Comparison with Other Techniques -- 4.2 Clinical Observation Assessment -- 5 Conclusion -- References. ; M U-Net: Intestine Segmentation Using Multi-dimensional Features for Ileus Diagnosis Assistance -- 1 Introduction -- 2 Method -- 2.1 Overview -- 2.2 Compound Loss Function with Deep Supervision -- 3 Experiments and Results -- 3.1 Experiment Detail -- 3.2 Results -- 4 Discussion and Conclusions -- References -- Enhancing Cardiac MRI Segmentation via Classifier-Guided Two-Stage Network and All-Slice Information Fusion Transformer -- 1 Introduction -- 2 Methods -- 2.1 Two-Stage Based Segmentation -- 2.2 All-Slice Fusion Transformer -- 2.3 Classifier-Guided Segmentation Refinement -- 2.4 Loss Functions -- 3 Experiments -- 3.1 Datasets and Settings -- 3.2 Results -- 3.3 Ablation Study -- 4 Discussion and Conclusion -- References -- Accessible Otitis Media Screening with a Deep Learning-Powered Mobile Otoscope -- 1 Introduction -- 2 Methodology -- 2.1 Mobile Otoscope -- 2.2 Deep Learning -- 2.3 Smartphone Application -- 3 Results -- 3.1 Algorithmic Testing -- 3.2 Validation Testing -- 4 Discussion and Conclusions -- 4.1 Prospect of Application -- References -- Feature Selection for Malapposition Detection in Intravascular Ultrasound - A Comparative Study -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Masking Irrelevant Regions -- 3.2 Including Temporal Context -- 3.3 Including Blood Flow Information via Deformation Fields -- 3.4 Combining the Modules -- 4 Experiments and Results -- 4.1 Training Modules Needed for Defining M and V -- 4.2 Single-Frame Classification -- 4.3 Classification Using Temporal Context -- 5 Conclusion -- References -- Author Index.
• Sommario/riassunto: This book constitutes the refereed proceedings of the first International Workshop on Applications of Medical Artificial Intelligence, AMAI 2023, held in conjunction with MICCAI 2023, in Vancouver, Canada in October 2023. The book includes 17 papers which were carefully reviewed and selected from 26 full-length submissions. The AMAI 2023 workshop created a forum to bring together researchers, clinicians, domain experts, AI practitioners, industry representatives, and students to investigate and discuss various challenges and opportunities related to applications of medical AI.
• Titolo autorizzato: Applications of Medical Artificial Intelligence
• ISBN: 3-031-47076-1
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996558465703316
• Lo trovi qui: Univ. di Salerno
• Serie: Lecture Notes in Computer Science, . 1611-3349 ; ; 14313