Artificial Intelligence in Medicine : 22nd International Conference, AIME 2024, Salt Lake City, UT, USA, July 9–12, 2024, Proceedings, Part II / / edited by Joseph Finkelstein, Robert Moskovitch, Enea Parimbelli

• Autore: Finkelstein Joseph
• Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024
• Edizione: 1st ed. 2024.
• Descrizione fisica: 1 online resource (387 pages)
• Disciplina: 006.3
• Soggetto topico: Artificial intelligence; Education - Data processing; Computer networks; Database management; Data mining; Application software; Artificial Intelligence; Computers and Education; Computer Communication Networks; Database Management; Data Mining and Knowledge Discovery; Computer and Information Systems Applications
• Altri autori: MoskovitchRobert ParimbelliEnea
• Nota di contenuto: -- Medical imaging analysis. -- 3T to 7T Whole Brain + Skull MRI Translation with Densely Engineered U-Net Network. -- A Sparse Convolutional Autoencoder for Joint Feature Extraction and Clustering of Metastatic Prostate Cancer Images. -- AI in Neuro-Oncology: Predicting EGFR Amplification in Glioblastoma from Whole Slide Images using Weakly Supervised Deep Learning. -- An Exploration of Diabetic Foot Osteomyelitis X-ray Data for Deep Learning Applications. -- Automated Detection and Characterization of Small Cell Lung Cancer Liver Metastases on CT. -- Content-Based Medical Image Retrieval for Medical Radiology Images. -- Cross-Modality Synthesis of T1c MRI from Non-Contrast Images Using GANs: Implications for Brain Tumor Research. -- Harnessing the Power of Graph Propagation in Lung Nodule Detection. -- Histology Image Artifact Restoration with Lightweight Transformer and Diffusion Model. -- Improved Glioma Grade Prediction with Mean Image Transformation. -- Learning to Predict the Optimal Template in Stain Normalization For Histology Image Analysis. -- MRI Brain Cancer Image Detection Application of an Integrated U-Net and ResNet50 Architecture. -- MRI Scan Synthesis Methods based on Clustering and Pix2Pix. -- Supervised Pectoral Muscle Removal in Mammography Images. -- TinySAM-Med3D: A Lightweight Segment Anything Model for Volumetric Medical Imaging with Mixture of Experts. -- Towards a Formal Description of Artificial Intelligence Models and Datasets in Radiology. -- Towards Aleatoric and Epistemic Uncertainty in Medical Image Classification. -- Ultrasound Image Segmentation via a Multi-Scale Salient Network. -- Data integration and multimodal analysis. -- A 360-Degree View for Large Language Models: Early Detection of Amblyopia in Children using Multi-View Eye Movement Recordings. -- Enhancing Anti-VEGF Response Prediction in Diabetic Macular Edema through OCT Features and Clinical Data Integration based on Deep Learning. -- Expert Insight-Enhanced Follow-up Chest X-Ray Summary Generation. -- Integrating multimodal patient data into attention-based graph networks for disease risk prediction. -- Integrative analysis of amyloid imaging and genetics reveals subtypes of Alzheimer progression in early stage. -- Modular Quantitative Temporal Transformer for Biobank-scale Unified Representations. -- Multimodal Fusion of Echocardiography and Electronic Health Records for the Detection of Cardiac Amyloidosis. -- Multi-View $k$-Nearest Neighbor Graph Contrastive Learning on Multi-Modal Biomedical Data. -- Quasi-Orthogonal ECG-Frank XYZ Transformation with Energy-based models and clinical text. -- Explainable AI. -- Do you trust your model explanations? An analysis of XAI performance under dataset shift. -- Explainable AI for Fair Sepsis Mortality Predictive Model. -- Explanations of Augmentation Methods For Deep Learning ECG Classification. -- Exploring the possibility of arrhythmia interpretation of time domain ECG using XAI: a preliminary study. -- Improving XAI Explanations for Clinical Decision-Making – Physicians’ Perspective on Local Explanations in Healthcare. -- Manually-Curated Versus LLM-Generated Explanations for Complex Patient Cases: An Exploratory Study with Physicians. -- On Identifying Effective Investigations with Feature Finding using Explainable AI: an Ophthalmology Case Study. -- Towards Interactive and Interpretable Image Retrieval-Based Diagnosis: Enhancing Brain Tumor Classification with LLM Explanations and Latent Structure Preservation. -- Towards Trustworthy AI in Cardiology: A Comparative Analysis of Explainable AI Methods for Electrocardiogram Interpretation.
• Sommario/riassunto: This two-volume set LNAI 14844-14845 constitutes the refereed proceedings of the 22nd International Conference on Artificial Intelligence in Medicine, AIME 2024, held in Salt Lake City, UT, USA, during July 9-12, 2024. The 54 full papers and 22 short papers presented in the book were carefully reviewed and selected from 335 submissions. The papers are grouped in the following topical sections: Part I: Predictive modelling and disease risk prediction; natural language processing; bioinformatics and omics; and wearable devices, sensors, and robotics. Part II: Medical imaging analysis; data integration and multimodal analysis; and explainable AI.
• Titolo autorizzato: Artificial Intelligence in Medicine
• ISBN: 9783031665356; 9783031665349
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910878044503321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Artificial Intelligence, . 2945-9141 ; ; 14845