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  • HCAI Group
    • Group
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    • Projects
      • NOE FTI-22-I-004 Infrastructure for Testing AI-driven robot systems in complex environments
      • #KANDINSKYPatterns our Swiss-Knife for the study of explainable-AI
        • KANDINSKY Challenge
      • FWF Project Reference Model of Explainable AI for the Medical Domain
      • EU Project HEAP – Human Exposome Assessment Platform
      • EU Project FeatureCloud (Federated Machine Learning)
      • Project MAKEpatho – Machine Learning & Knowledge Extraction in Digital Pathology
      • Project TUGROVIS – Tumor-Growth Simulation and Visualization
      • Project GRAPHINIUS – Interactive Graph Research Framework
      • Project iML interactive Machine Learning with the Human-in-the-Loop
        • Experiment: Human Intelligence vs. Artificial Intelligence in Pattern Recognition
        • Experiment: Gamification of interactive Machine Learning (giML)
        • Experiment: Interactive Machine Learning for the Traveling-Salesman-Problem
      • Project EMPAIA – Ecosystem for Pathology Diagnostics with AI Assistance
      • AUGMENTOR
    • Publications
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    • General
      • International Expert Network
      • Relevant Conferences
      • Relevant Journals (selected)
    • In Progress
      • 7-sins-of-medical-AI
    • Published
      • Special Issue AI for Life
      • Springer LNAI xxAI – Beyond explainable Artificial Intelligence
      • Springer LNAI 12090 – AI/Machine Learning for Digital Pathology
      • LNAI 9605 Machine Learning for Health Informatics
      • LNAI Hot Topics in integrative Machine Learning & Knowledge Extraction (iMAKE)
      • Big Data of Complex Networks
      • SI Machine Learning and Entropy
      • LNCS 8700 Smart Health
      • SI Information Processing (IPM)
      • SI BMC Bioinformatics
      • Pervasive Health
      • LNCS 8401 Interactive KDD in Biomedical Informatics
    • Upcoming Events
      • Extravaganza call for papers: Hot Topics in Machine Learning and Knowledge Extraction
    • Past Events
      • Monday, November 7, 2022, 18:00 UFT Tulln/Donau: Antrittsvorlesung Andreas Holzinger
      • HCAI Lab Symposium “Digital Transformation in Smart Farm and Forest Operations”, August, 22, 2022
      • Explainable AI xAI 2021
      • XXAI @ ICML 2020 Extending Explainable AI Beyond Deep Models and Classifiers
      • Explainable AI xAI 2020
      • AI/ML SYMPOSIUM Graz, June, 6, 2019
      • Explainable AI conference session exAI 2019
      • WS Secure Federated Machine Learning for Health Informatics, March, 1-2, 2018
      • 11@Hamburg – CD-MAKE 2018
      • 10@Reggio – Privacy Aware Machine Learning
      • 09@Salzburg – Privacy Aware Machine Learning
      • WS Machine Learning for Biomed @ TUGraz Jan, 26, 2016
      • Machine Learning for Health TUW
      • 08@London i Machine Learning
      • 07@Banff – Knowledge Discovery
      • 06@Warsaw – interactive KDD
      • 05@Lisbon – interactive KDD
      • 04@Regensburg – interactive KDD
      • 03@Maribor > Interactive KDD
      • 02@Macau – interactive KDD
      • 01@Graz HCI-KDD Kick-Off
  • For Students
    • Courses
      • 120101 BOKU – AI for Life Sciences (WiSe 2023/24)
    • Past Courses
      • LV 706.046 AK HCAI Mini Projects (Class of summer term 2023)
      • Open Student Work 2023 (Project, Bacc, Master, …)
      • HCAI Research Seminar (class of 2021/22)
      • HCAI Embodied Intelligence Seminar (class of 2022/23)
      • LV 706.046 AK HCAI Mini Projects (Class of 2021)
      • LV 185.A83 Machine Learning for Health Informatics (Class of 2021)
      • HCAI Research Seminar (class of 2020/21)
      • Mini Course Medical AI (2020)
      • LV 185.A83 Machine Learning for Health Informatics (Class of 2020)
      • Seminar xAI (class of 2019)
      • LV 185.A83 Machine Learning for Health Informatics (Class of 2019)
      • LV 706.046 AK HCI xAI (class of 2020)
      • LV 706.315 From explainable AI to Causability (class of 2019)
      • Mini Course MAKE-Decisions – with practice (class of 2019)
      • LV 706.046 AK HCI 2019: Intelligent UI: towards explainable AI
      • Mini Course: From Data Science to interpretable AI (class of 2019)
      • Mini Course MAKE-Decisions – with practice (WS 2018)
      • LV 706.046 AK HCI 2018: Intelligent UI: to explainable AI
      • LV 185.A83 Machine Learning for Health Informatics class 2018
      • LV 709.049 Biomedical Informatics
      • Mini Course MAKE-Decisions
      • LV 185.A83 Machine Learning for Health Informatics class 2017
      • Mini-Course Machine Learning Knowledge Extraction Verona
      • LV 706.046 AK HCI: Intelligent UI with Challenge 2017
      • LV 706.315 Interactive Machine Learning (iML)
      • LV 706.997/998 PhD Seminar Welcome Students
      • LV 706.046 Selected Topics of HCI: Intelligent UI
      • LV 185.A83 Machine Learning for Health Informatics class 2016
      • LV 340.300 Principles of Interaction – iML
      • LV 706.315 Methods of explainable AI (ex-AI class 2018)
    • Resources
      • Learning Machine Learning
      • Open Data Sets
      • Open Code
  • Open Jobs (2024)
  • Partners
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Projects

AUGMENTOR

The Augmentor is a widely accepted data augmentation library for machine learning, deep learning, in Python and Julia

Project EMPAIA – Ecosystem for Pathology Diagnostics with AI Assistance

EMPAIA stands for Ecosystem for Pathology Diagnostics with AI Assistance
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  • Experiment: Interactive Machine Learning for the Traveling-Salesman-Problem

Latest News

  • SURVEY – THE SEVEN DEADLY SINS OF AI IN MEDICINE2024-07-16 - 19:03

    Participate in a crucial survey examining key ethical challenges in medical AI.
    Your insights are invaluable in shaping a future of health Artificial Intelligence !

  • Enhancing trust in automated 3D point cloud data interpretation through explainable counterfactuals2025-05-02 - 11:22

    Our most recent paper introduces a novel framework for augmenting explainability in the interpretation of point cloud data by fusing expert knowledge with counterfactual reasoning. Given the complexity and voluminous nature of point cloud datasets, derived predominantly from LiDAR and 3D scanning technologies, achieving interpretability remains a significant challenge, particularly in smart cities, smart agriculture, […]

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