Overview

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.

Full Product Details

Author:   Joseph Finkelstein ,  Robert Moskovitch ,  Enea Parimbelli
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   2024 ed.
Volume:   14845
ISBN:  

9783031665349

ISBN 10:   3031665341
Pages:   366
Publication Date:   27 July 2024
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

.- 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.

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