Since 2009, we have led the ENIGMA Consortium, a worldwide alliance of over 2,000 scientists studying 30 brain diseases in 45 countries. Pooling multimodal brain images and genomic data from over 100,000 people worldwide, ENIGMA's 51 working groups have published the largest neuroimaging studies of schizophrenia, bipolar disorder, depression, PTSD, addiction, and Parkinson’s disease, OCD, ADHD, epilepsy, HIV and autism. ENIGMA’s genomic screens of more than 70,000 people’s brain scans and genome-wide data have brought together experts from 300 institutions to unearth genetic variants that affect brain structure, connectivity, functional brain synchrony, and disease risk. In this talk, we cover current and future challenges in analyzing medical imaging and genomic data worldwide. We describe the vast power of AI and deep learning methods for disease diagnosis, subtyping, and staging, and for discovering environmental, medication, and genomic factors that promote or resist disease. We illustrate the talk with mathematical examples from meta-analysis, AI and federated algorithms for multimodal image harmonization, predictive modeling, and even novel image synthesis and enhancement.
In the context of machine learning for uncertainty quantification (UQ) of inverse problems: we propose to first transform input observations using the adjoint. We demonstrate with two imaging examples (photoacoustic imaging and CT) that firstly: the adjoint partially undoes the physics of the problem resulting in faster convergence of the learning phase and secondly: the final algorithm is now robust to changes in observations such as changing transducer subsampling in photoacoustic imaging and angles in CT. Our adjoint-based method gives point estimates faster than traditional baselines and with higher SSIM metrics, while also providing validated UQ.
Peripapillary atrophy (PPA), a type of aberrant retinal symptom frequently present in older individuals or people with myopia, might indicate the severity of glaucoma or myopia. It is particularly beneficial for diagnosis when PPA is segmented effectively in fundus images. Deep learning is now frequently used for PPA segmentation. However, previous segmentation algorithms frequently mix up PPA with its neighboring tissue, the optic disc (OD), and generate the incorrect PPA area even though PPA is not present in the fundus image. To address these problems, we propose an improved segmentation network based on multi-task learning by combining detection and segmentation of PPA. We analyze the shortcomings of widely used loss functions and define a modified one to guide the training process of the network. We design a three-class segmentation task by introducing the information of OD, forcing the network to learn the difference of characteristics between OD and PPA. Evaluation on a clinical dataset shows that our method achieves an average Dice coefficient of 0.8854 in PPA segmentation, outperforming UNet and TransUNet, two state-of-the-art methods, by 24.4% and 10.6%, respectively.
We propose to apply model-agnostic meta-learning (MAML) and MAML++ for pathology classification from optical coherence tomography (OCT) images. These meta-learning methods train a set of initialization parameters using training tasks, by which the model achieves fast convergence in new tasks with only a small amount of data. Our model is pretrained on an OCT dataset with seven types of retinal pathologies, and then refined and tested on another dataset with three types of pathologies. The classification accuracies of MAML and MAML++ reached 90.60% and 95.60% respectively, which are higher than the traditional deep learning method with pretraining.
Convolutional neural networks (CNN) are a powerful deep learning method for medical image segmentation. However performance in clinical practice often deteriorates when attempting to generalize models trained from a particular source domain to a different target domain (e.g. different vendor, acquisition parameters, protocols). To address this issue, domain adaptation has attracted increasing attention because it can minimize distribution differences among different but related domains. Extending from this prior work, we introduce Co-Unet-GAN, a co-learning domain adaptation and segmentation model addressing the domain shift problem. In this model, we train a Unet segmentation network and an image translation generative adversarial network (GAN) together to generalize performance across domains given supervised data only in the source domain. We evaluate our model on two large open echocardiography datasets, using the CAMUS set as supervised source domain and EchoNet-Dynamic as the unsupervised target. In this context, we obtain mean absolute error on ejection fraction of 9.67% on Co-Unet-GAN compared to 11.28% for a previously published Unet-GAN. Our Co-Unet-GAN for image translation and segmentation is a promising solution to the domain shift problem.
