For the end of our Summer School, we offer participants from all backgrounds to present their own work, studies, or company to the community. Here is the list of the talks:
Josephine Lamp
GlucoSynth: Generating Differentially-Private Synthetic Glucose Traces
Sherif Gonem
Dynamic early warning scores to predict clinical deterioration in respiratory hospital patients
Kenneth Lui
Medical AI opportunities and lesson learnt in a public healthcare organization
Kexin Xu
Predicting Biological Age Using DNA Methylation & Metabolites
Varsha Kesavan
Generative Restoration for Motion Correction in Retrospective Free-Breathing Cardiac MRI
Nick Rucks
An Explainable Deep Learning Approach for Mortality Prediction in Allogeneic Stem Cell Transplantation
Artsiom Hramyka
Follicle profile dynamics in assisted conception: an explainable AI model
Alexander Janssen
Learning latent effects to optimize vancomycin treatment in the critically-ill
Abder-Rahman Ali
Self-supervised Learning for Liver Ultrasound Imaging
Iman Deznabi
MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Time Series Prediction
Luigi A. Moretti
Integrating Affective Computing into Treatment Pathways for Anxiety Disorders
Vincent Holstein
Remote monitoring of depression severity
Maximilian Hildebrandt
Large-Scale Systematic Evaluation of Blood Glucose Forecasting Algorithms in Diabetes Mellitus
Aman Sinha
Modeling Irregularly Sampled Time Series without Imputation
Gauranga Kumar Baishya & Mehak Singal
Multimodal Single Cell Integration
Abdulwhhab Abu Alamrain
Personal Experience in Medical Research: Obstacles and Opportunities
Jolanta Mozyrska
Cardiac anatomy modelling with Latent Diffusion Models
Belona Sonna
Formal XAI and fairness in AI-based decision making
Crina Samarghitean
Digital Twins in Healthcare
Maurizio Sessa
Machine learning-driven development of a disease risk score for COVID-19 hospitalization and mortality: A Swedish and Norwegian register-based study
Sudarshan Sreeram
Accelerating on-device medical imaging using compressed deep learning models
Shery Huang & Marzia Calcagno
3D printing & neural network co-modelling (3PNN) for Physically-Informed and Interpretable ML
Lora Frayling
Sharing privacy-preserving synthetic data to support healthcare research
Aya El Mir
Bias in Medical Imaging; Case Study predicting gender from ChestXrays and Shortcut learning
Mohamed Ragab Mohamed Adam
Source-free domain adaptation via Temporal Imputation for Time Series Data
Evgeny Galimov
Comparative effectiveness of sotrovimab versus no treatment in non-hospitalised high-risk patients with COVID-19 in England: a retrospective cohort study using the Discover-Now database
