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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.
.- 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.