Artificial Intelligence in Medicine

22nd International Conference, AIME 2024, Salt Lake City, UT, USA, July 9-12, 2024, Proceedings, Part II
2024
ISBN: 978-3-031-66534-9
Verlag: Springer Nature Switzerland

Buch, Englisch, Band 14845, 366 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 598 g

Reihe: Lecture Notes in Computer Science

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.

Zielgruppe

Research

Autoren/Hrsg.

Finkelstein, Joseph

Parimbelli, Enea

Moskovitch, Robert

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Angewandte Informatik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Sozialwissenschaften Pädagogik Lehrerausbildung, Unterricht & Didaktik E-Learning, Bildungstechnologie
• Mathematik | Informatik EDV | Informatik Technische Informatik Netzwerk-Hardware

Inhaltsverzeichnis

.- 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.