Author: Yu-Wei Chang

Synthesizing tau pathology from structural brain imaging using deep learning
Yu-Wei Chang, Giovanni Volpe, Joana B Pereira
Date: 22 August 2023
Time: 10:15 AM PDT

In vivo tau-positron emission tomography (PET) is crucial for determining the stage of Alzheimer’s disease (AD). However, this method is expensive, not widely available, and exposes patients to ionizing radiation, which poses a carcinogenic risk. To address this issue, I’ll present our proposed method, a deep-learning synthesis approach for follow-up tau-PET brain images from baseline tau-PET images using a generative adversarial network (GAN). This technique has the potential to provide valuable insights into the progression of AD, the effectiveness of new treatments, and more accurate diagnosis of the disease.

 

Training of neural network with incomplete medical datasets
Yu-Wei Chang

Neural network training and validation rely on the availability of large high-quality datasets. However, in many cases, only incomplete datasets are available, particularly in health care applications, where each patient typically undergoes different clinical procedures or can drop out of a study. Here, we introduce GapNet, an alternative deep-learning training approach that can use highly incomplete datasets without overfitting or introducing artefacts. Using two highly incomplete real-world medical datasets, we show that GapNet improves the identification of patients with underlying Alzheimer’s disease pathology and of patients at risk of hospitalization due to Covid-19. Compared to commonly used imputation methods, this improvement suggests that GapNet can become a general tool to handle incomplete medical datasets.

Date: 31 May 2023
Time: 10:15
Place: MC2 Kollektorn
Event: AI for Scientific Data Analysis: Miniconference

Deep-learning analysis in tau PET for Alzheimer’s continuum
Yu-Wei Chang, Giovanni Volpe, Joana B Pereira
Submitted to SPIE-ETAI
Date: 24 August 2022
Time: 16:40 (PDT)

Previous studies have suggested that Alzheimer’s disease (AD) is typically characterized by abnormal accumulation of tau proteins in neurofibrillary tangles. This is usually assessed by measuring tau levels in regions of interest (ROIs) defined based on previous post-mortem studies. However, it remains unclear where this approach is suitable for assessing tau accumulation in vivo across the different stages of individuals. This study employed a data-driven deep learning approach to detect tau deposition across different AD stages at the voxel level. Moreover, the classification performance of this approach on distinguishing different AD stages was compared with the one using conventional ROIs.

Neural network training with highly incomplete medical datasets
Yu-Wei Chang, Laura Natali, Oveis Jamialahmadi, Stefano Romeo, Joana B Pereira, Giovanni Volpe
Submitted to SPIE-ETAI
Date: 24 August 2022
Time: 08:00 (PDT)

Neural network training and validation rely on the availability of large high-quality datasets. However, in many cases, only incomplete datasets are available, particularly in health care applications, where each patient typically undergoes different clinical procedures or can drop out of a study. Here, we introduce GapNet, an alternative deep-learning training approach that can use highly incomplete datasets without overfitting or introducing artefacts. Using two highly incomplete real-world medical datasets, we show that GapNet improves the identification of patients with underlying Alzheimer’s disease pathology and of patients at risk of hospitalization due to Covid-19. Compared to commonly used imputation methods, this improvement suggests that GapNet can become a general tool to handle incomplete medical datasets.