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Artificial Intelligence in Medicine 22nd International Conference, AIME 2024, Salt Lake City, UT, USA, July 9–12, 2024, Proceedings, Part II

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 revie...

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Bibliographic Details
Other Authors: Finkelstein, Joseph (Editor), Moskovitch, Robert (Editor), Parimbelli, Enea (Editor)
Format: eBook
Language:English
Published: Cham Springer Nature Switzerland 2024, 2024
Edition:1st ed. 2024
Series:Lecture Notes in Artificial Intelligence
Subjects:
Database Management
Data Mining
Artificial Intelligence
Computers And Education
Education / Data Processing
Data Mining And Knowledge Discovery
Application Software
Computer Communication Networks
Computer Networks 
Computer And Information Systems Applications
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Table of Contents:
  • 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
  • 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.

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