Cantitate/Preț
Produs
Total produse
Vezi coșul
Artificial Intelligence in Medicine: 22nd International Conference, AIME 2024, Salt Lake City, UT, USA, July 9–12, 2024, Proceedings, Part II de Joseph Finkelstein

Artificial Intelligence in Medicine: 22nd International Conference, AIME 2024, Salt Lake City, UT, USA, July 9–12, 2024, Proceedings, Part II: Lecture Notes in Computer Science, cartea 14845

Editat de Joseph Finkelstein, Robert Moskovitch, Enea Parimbelli
en Limba Engleză Paperback – 5 sep 2024
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.
Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (2) 43264 lei  6-8 săpt.
  Springer Nature Switzerland – 5 sep 2024 43264 lei  6-8 săpt.
  Springer Nature Switzerland – 5 sep 2024 43512 lei  6-8 săpt.

Din seria Lecture Notes in Computer Science

Preț: 43264 lei

Preț vechi: 54080 lei
-20%

Puncte Express: 649
Preț estimativ în valută:
7652€ • 8945$ • 6653£

Carte tipărită la comandă

Livrare economică 07-21 martie

 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă

Specificații

ISBN-13: 9783031665349
ISBN-10: 3031665341
Pagini: 366
Ilustrații: XVIII, 356 p. 112 illus.
Dimensiuni: 155 x 235 mm
Ediția:2024
Editura: Springer Nature Switzerland
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence

Locul publicării:Cham, Switzerland

Cuprins

.- 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.