Parkinson disease (PD) is the second most common neurodegenerative disorder, and, owing to the aging population, the number of patients with PD is expected to double by 2030.¹ Projected PD prevalence will be more than 1.6 million in the US with projected total economic burden surpassing $79 billion by 2037.² The diagnosis of PD is based on the clinical manifestations and findings from complex neuroimaging examinations, such as magnetic resonance imaging (MRI) or magnetic resonance angiography (MRA), which contribute to medical burden among patients with PD. Therefore, besides the established diagnostic method, many studies have been conducted to investigate the potential metrics for PD, translating these into clinically practical tools for the diagnosis and treatment of patients with PD.

Drusen are the main aspect of detecting age-related macular degeneration (AMD). Ophthalmologists can evaluate the condition of AMD based on drusen in fundus images. However, in the early stage of AMD, the drusen areas are usually small and vague. This leads to challenges in the drusen segmentation task. Moreover, due to the high-resolution fundus images, it is hard to accurately predict the drusen areas with deep learning models. In this paper, we propose a multi-scale deep learning model for drusen segmentation. By exploiting both local and global information, we can improve the performance, especially in the early stages of AMD cases.

In the autonomous driving environment, object instances in an image can be affected by various factors such as camera, driving state, weather, and system component. However, the deep learning‐based vision systems are vulnerable to perturbation, which contains noise. Thus, robust object detection under harsh autonomous‐driving environments is a more difficult than the generic situation. In this paper, it is found that not only the accuracy, but also the speed of the non‐maximum suppression‐based detector can be degraded under harsh environments. Therefore, object detection is handled under a harsh situation with adversarial mechanisms such as adversarial training and adversarial defence. Adversarial defence modules are designed to improve robustness in feature extraction level and define perturbations under a harsh environment for training object detectors to improve the robustness of the model's decision boundary. The proposed adversarial defence and training mechanisms improve the object detector in both accuracy and speed. The proposed method shows a 43.7% mean average precision for the COCO2015 dataset in generic object detection and 39.0% mean average precision for the BDD100K dataset in a driving environment. Furthermore, it achieves a real‐time capability of 23 frames per second.