Cover: Artificial Intelligence in Healthcare (E-Book) von Hao Ni
NEU
Hao Ni

Teil der Reihe: Springer Nature Proceedings Computer Science

Artificial Intelligence in Healthcare

Third International Conference, AIiH 2026, London, UK, August 26-28, 2026, Proceedings, Part III
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PDFThird International Conference, AIiH 2026, London, UK, August 26-28, 2026, Proceedings, Part I - 641 Seiten €106,99 PDFThird International Conference, AIiH 2026, London, UK, August 26-28, 2026, Proceedings, Part III - 407 Seiten €78,10 Aktuell
Inhaltsangabe

.- Medical image analysis and processing

.- Benchmarking Foundation Models for Pulmonary Embolism Diagnosis and Prognosis to Reveal Demographic Disparities.
.- Phase-Guided Quality Regression in Apical Echocardiography via CNN-LSTM: A Systematic Backbone Benchmarking Study.
.- A Dual-Domain Grayscale-Wavelet Reparameterization Network for Upper-Limb Long-Bone Abnormality Detection on X-ray Images.

.- GAZE: Grounded Agentic Zero-shot Evaluation with Viewer-Level Tools and Literature Retrieval on Rare Brain MRI.
.- Workflow-Level ROI Restriction for Deep Learning-Based 3D Aortic Valve Segmentation in Non-Contrast Cardiac CT.
.- General or Medical CLIP, Which one Shall We Choose?.
.- Cross-View Attention Routing in Capsule Networks for Structured Interpretable Multi-View Mammogram Breast Cancer Classification.
.- A Cascade YOLO-Based Approach for Robust Retinal Optic Disc and Optic Cup Segmentation.
.- XSSR: Cross-Domain Self-Supervised Representative Selection for Efficient Annotation in Medical Image Segmentation.
.- Regional Myocardial Strain Estimation with MyoTrackerChrono.
.- Benchmarking deep learning and conventional methods for 2D and 3D lung tumour segmentation in CT images.
.- Gumbel-Sigmoid Based Adaptive Sparse Hypergraph Learning for Brain Structural Connection Analysis.
.- A Deep Learning Pipeline for Diabetic Foot Ulcer Detection and Stage Classification.
.- Contrastive Learning for Acne Severity Grading with Small-Scale Datasets.
.- Adversarial Attacks vs.Data Augmentation Strategies: A Comprehensive Comparative Study with Application to Diabetic Retinopathy Detection.
.- Towards Fair Radiographic AI: Representation and Classification Bias Analysis.
 
.- AI-aided medical imaging
 
.- Evaluating the trustworthiness of the Fréchet Inception Distance with stochastic embedding representations.
.- Multi-Expert Consensus as a Label-Free Quality Surrogate for Echocardiographic LV Segmentation.
.- Transformer Architectures Improve Minority-Class Detection in Histopathological Classification of Colorectal Polyps.
 
.- AI in mental health
 
.- Empathic Structured Exploration Model (ESEM): A Conversational Framework for Mental Health Screening Chatbots.
.- A Scalable LLM-Based System for Early Mental Health Intervention and Workforce Resilience Using Genkit.
.- Performance Evaluation of LLMs for Psychological Assessment.
 
.- AI in proactive health management
 
.- When LLMs guide the Plate: Exploring Diet Recommendations for Mental Health Support.
.- AI–based Approaches for Diabetes Management in Africa: Educational, Self-Management, and Adherence-Oriented Interventions.
 
.- Healthcare workflow optimisation and automation
 
.- Reducing Outpatient No-Shows with Adaptive Reminder Strategies: A Simulation Framework for Contextual Bandit Learning and Data Requirements.
.- AI-Assisted Rare Metabolic Disease Screening from Targeted LC-HR-MS: A Human-in-the-Loop Pipeline with Synthetic Data Augmentation.
.- Beyond Accuracy: A Decision-Theoretic Framework for Allocation-Aware Healthcare AI.
 
.- DoRa-PVS Challenge: The Domain Randomisation Challenge for Perivascular Space Segmentation
 
.- Assessing Generalisation of Perivascular Space Segmentation Across Heterogeneous MRI Cohorts: The DoRA-PVS Challenge 2026.
Produktdetails
  • Erscheinungsdatum: 11.08.2026
  • Autor/Autorin: Hao Ni
  • Format: E-Book
  • Dateiformat: PDF
  • Kopierschutz: Wasserzeichen
  • Dateigröße: 41.2 MB
  • Verlag: SPRINGER
  • Sprache: Englisch
  • Umfang: 407 Seiten
  • ISBN: 9783032353931
  • Lieferung: Sofort per Download
  • Hinweis: Sofort per Download lieferbar. Kein physischer Versand.
  • Kompatibilität: Lesbar auf Geräten und Apps mit PDF-Unterstützung.
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