Jens Kleesiek
Professor of Translational Image-guided Oncology
Institute for AI in Medicine, Germany
The Focus of Jens’s research is on applying self-supervised and weakly supervised learning paradigms to recognize clinically relevant patterns in large and complex data and the integration of multimodal data sources to enhance the decision-making process at the point of care. He studied medicine in Heidelberg and bioinformatics in Hamburg, where he obtained a Ph.D. in computer science in 2012. After training at the University Hospital Heidelberg and the German Cancer Research Center (DKFZ), he received his board certification (Facharzt) in radiology, his habilitation (venia legendi), and specialization (Zusatzweiterbildung) in medical informatics.
Latest
- GAN-based generation of realistic 3D volumetric data: A systematic review and taxonomy
- Advancing Healthcare Through Open Science: StudierFenster and MedShapeNet
- Characteristics and outcomes of patients (pts) with metastatic castration-resistant prostate cancer (mCRPC) treated with lutetium-177–PSMA-617 (PSMA-RLT) in a real-world setting.
- Deep PCCT: Photon Counting Computed Tomography Deep Learning Applications Review
- Metrics reloaded: recommendations for image analysis validation
- Understanding metric-related pitfalls in image analysis validation
- ChatGPT in healthcare: A taxonomy and systematic review
- Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images
- CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images
- Enhanced Diagnostic Fidelity in Pathology Whole Slide Image Compression via Deep Learning
- k-strip: A novel segmentation algorithm in k-space for the application of skull stripping
- What is Diminished Virtuality? A Directional and Layer-Based Taxonomy for the Reality-Virtuality Continuum
- 3D surgical instrument collection for computer vision and extended reality
- 3D-COSI ~ 3D Collection of Surgical Instruments
- Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence
- Anatomy Completor: A Multi-class Completion Framework for 3D Anatomy Reconstruction
- Guiding the Guidance: A Comparative Analysis of User Guidance Signals for Interactive Segmentation of Volumetric Images
- Improvement in Disease Diagnosis in Computed Tomography Images by Correlating Organ Volumes with Disease Occurrences in Humans
- Medical Foundation Models are Susceptible to Targeted Misinformation Attacks
- Virtual Reality, Augmented Reality, Augmented Virtuality, or Mixed Reality in Cardiopulmonary Resuscitation: Which Extended Reality am I Using for Teaching Adult Basic Life Support?
- Apple Vision Pro for Healthcare: \"The Ultimate Display\"?
- Challenges for AI Regulation in Health and for Healthcare Organizations: Notes from the University of Florida's NSF-Sponsored Workshop on AI Governance
- Histology-Based Prediction of Therapy Response to Neoadjuvant Chemotherapy for Esophageal and Esophagogastric Junction Adenocarcinomas Using Deep Learning
- Intraoperative Molecular Positron Emission Tomography Imaging for Intraoperative Assessment of Radical Prostatectomy Specimens
- Kommentar zu KI – Sarkoidose oder Lymphom? Maschinelles Lernen performt gut
- MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
- Information extraction from weakly structured radiological reports with natural language queries
- Open-source skull reconstruction with MONAI
- Towards Unifying Anatomy Segmentation: Automated Generation of a Full-body CT Dataset via Knowledge Aggregation and Anatomical Guidelines
- Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling
- CellViT: Vision Transformers for Precise Cell Segmentation and Classification
- Establishing Medical Intelligence - Leveraging FHIR to Improve Clinical Management
- The autoPET challenge: Towards fully automated lesion segmentation in oncologic PET/CT imaging
- Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the AutoImplant 2021 cranial implant design challenge
- Valuing vicinity: Memory attention framework for context-based semantic segmentation in histopathology
- An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids
- An Opinion on ChatGPT in Health Care—Written by Humans Only
- ChatGPT in Healthcare: A Taxonomy and Systematic Review
- Histology-based Prediction of Therapy Response to Neoadjuvant Chemotherapy for Esophageal and Esophagogastric Junction Adenocarcinomas Using Deep Learning
- Mirror U-Net: Marrying Multimodal Fission with Multi-task Learning for Semantic Segmentation in Medical Imaging
- A web-based radiomics module for image feature extraction for tumor characterization
- Challenges in Implementing the Local Node Infrastructure for a National Federated Machine Learning Network in Radiology
- Contrast Agent Dose Reduction in MRI Utilizing a Generative Adversarial Network in an Exploratory Animal Study
- Das Netzwerk Universitätsmedizin: Technisch-organisatorische Ansätze für Forschungsdatenplattformen
- Evaluation of thresholding methods for the quantifcation of 68Ga‑PSMA‑11 PET molecular tumor volume and their efect on survival prediction in patients with advanced prostate cancer undergoing 177Lu‑PSMA‑617 radioligand therapy
- Lean Study Host: Towards an Automated Pipeline for Multi-Center Study Hosting
- Model Generalizability Investigation for GFCE-MRI Synthesis in NPC Radiotherapy Using Multi-institutional Patient-based Data Normalization
- Multimodal Interactive Lung Lesion Segmentation: A Framework for Annotating PET/CT Images based on Physiological and Anatomical Cues
- Novel measures for the diagnosis of hepatic steatosis using contrast-enhanced computer tomography images
- On the Impact of Cross-Domain Data on German Language Models
- Prediction of Bone Marrow Biopsy Results From MRI in Multiple Myeloma Patients Using Deep Learning and Radiomics
- The HoloLens in medicine: A systematic review and taxonomy
- Current State of Community-Driven Radiological AI Deployment in Medical Imaging
- Deep learning-based assessment of body composition and liver tumor burden for survival modeling in advanced colorectal cancer
- Open-Source Skull Reconstruction with MONAI
- 'A net for everyone': fully personalized and unsupervised neural networks trained with longitudinal data from a single patient
- Detailed Annotations of Chest X-Rays via CT Projection for Report Understanding
- Model Generalizability Investigation for GFCE-MRI Synthesis in Radiotherapy of NPC patients Using Multi-institutional Data and Patient-based Data Normalization
- Valuing Vicinity: Memory attention framework for context-based semantic segmentation in histopathology
- AutoPET Challenge: Combining nn-Unet with Swin UNETR Augmented by Maximum Intensity Projection Classifier
- Biomarkers extracted by fully automated body composition analysis from chest CT correlate with SARS-CoV-2 outcome severity
- The HoloLens in Medicine: A systematic Review and Taxonomy
- Training beta-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder
- CT-derived body composition analysis could possibly replace DXA and BIA to monitor NET-patients
- Low bone mineral density is a prognostic factor for elderly patients with HCC undergoing TACE: results from a multicenter study
- Artificial intelligence guided enhancement of digital PET: scans as fast as CT?
- Deep Learning–based Assessment of Oncologic Outcomes from Natural Language Processing of Structured Radiology Reports
- Deep Learning–driven classification of external DICOM studies for PACS archiving
- GAN-based generation of realistic 3D data: A systematic review and taxonomy
- Metrics reloaded: Pitfalls and recommendations for image analysis validation
- Quantification and reduction of cross-vendor variation in multicenter DWI MR imaging: results of the Cancer Core Europe imaging task force
- k-strip: A novel segmentation algorithm in k-space for the application of skull stripping
- Medical Deep Learning – A systematic Meta-Review
- Jointly Optimized Deep Neural Networks to Synthesize Monoenergetic Images from Single-Energy CT Angiography for Improving Classification of Pulmonary Embolism
- Pseudoprospective Paraclinical Interaction of Radiology Residents With a Deep Learning System for Prostate Cancer Detection: Experience, Performance, and Identification of the Need for Intermittent Recalibration
- Review of Disentanglement Approaches for Medical Applications: Towards Solving the Gordian Knot of Generative Models in Healthcare
- Sparse Convolutional Neural Networks for Medical Image Analysis
- Virtual Biopsy: Just an AI Software or a Medical Procedure?
- AIMHI: Protecting Sensitive Data through Federated Co-Training
- Beyond Medical Imaging-A Review of Multimodal Deep Learning in Radiology
- Breaking with Fixed Set Pathology Recognition Through Report-Guided Contrastive Training
- CODEX Meets RACOON – A Concept for Collaborative Documentation of Clinical and Radiological COVID-19 Data
- Combining deep learning and radiomics for automated, objective, comprehensive bone marrow characterization from whole-body MRI: a multicentric feasibility study
- Contrast Media Reduction in Computed Tomography With Deep Learning Using a Generative Adversarial Network in an Experimental Animal Study
- DICOM Whole Slide Imaging for Computational Pathology Research in Kaapana and the Joint Imaging Platform
- Fine-tuning BERT Models for Summarizing German Radiology Findings
- In Vivo Repeatability and Multiscanner Reproducibility of MRI Radiomics Features in Patients With Monoclonal Plasma Cell Disorders
- Introduction to Machine Learning: Definitions and Hybrid Imaging Applications
- Radiomics in Head and Neck Cancer Outcome Predictions
- Structured Knowledge Graphs for Classifying Unseen Patterns in Radiographs
- Reference-guided Pseudo-Label Generation for Medical Semantic Segmentation
- Vesseg: An Open-Source Tool for Deep Learning-Based Atherosclerotic Plaque Quantification in Histopathology Image.
- CT Angiography Clot Burden Score from Data Mining of Structured Reports for Pulmonary Embolism
- Discovering Digital Tumor Signatures—Using Latent Code Representations to Manipulate and Classify Liver Lesions
- The Federated Tumor Segmentation (FeTS) Challenge
- A reporting and analysis framework for structured evaluation of COVID-19 clinical and imaging data
- Common Limitations of Image Processing Metrics: A Picture Story
- A Relational-learning Perspective to Multi-label Chest X-ray Classification
- Adapting Bidirectional Encoder Representations from Transformers (BERT) to Assess Clinical Semantic Textual Similarity: Algorithm Development and Validation Study
- Prediction of low-keV monochromatic images from polyenergetic CT scans for improved automatic detection of pulmonary embolism
- Ai-based aortic vessel tree segmentation for cardiovascular diseases treatment: status quo
- Künstliche Intelligenz in der onkologischen Radiologie: Ein (P)review
- MOMO--Deep Learning-driven classification of external DICOM studies for PACS archivation
- Joint Imaging Platform for Federated Clinical Data Analytics
- Self-Guided Multiple Instance Learning for Weakly Supervised Thoracic DiseaseClassification and Localizationin Chest Radiographs
- PSMA PET total tumor volume predicts outcome of patients with advanced prostate cancer receiving [177Lu]Lu-PSMA-617 radioligand therapy in a bicentric analysis
- Semi-automatically quantified tumor volume using Ga-68-PSMA-11-PET as biomarker for survival in patients with advanced prostate cancer
- Künstliche Intelligenz in der Hybridbildgebung
- Analysis of PSMA expression and outcome in patients with advanced Prostate Cancer receiving $^textrm177$ Lu-PSMA-617 Radioligand Therapy
- Wie funktioniert maschinelles Lernen?
- Wie funktioniert Radiomics?
- Künstliche Intelligenz und maschinelles Lernen in der onkologischen Bildgebung
- Can Virtual Contrast Enhancement in Brain MRI Replace Gadolinium?: A Feasibility Study