Yiyu Cai

Bo Qian, Hao Chen, Xiangning Wang et al.

Computer-assisted automatic analysis of diabetic retinopathy (DR) is of great importance in reducing the risks of vision loss and even blindness. Ultra-wide optical coherence tomography angiography (UW-OCTA) is a non-invasive and safe imaging modality in DR diagnosis system, but there is a lack of publicly available benchmarks for model development and evaluation. To promote further research and scientific benchmarking for diabetic retinopathy analysis using UW-OCTA images, we organized a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). The challenge consists of three tasks: segmentation of DR lesions, image quality assessment and DR grading. The scientific community responded positively to the challenge, with 11, 12, and 13 teams from geographically diverse institutes submitting different solutions in these three tasks, respectively. This paper presents a summary and analysis of the top-performing solutions and results for each task of the challenge. The obtained results from top algorithms indicate the importance of data augmentation, model architecture and ensemble of networks in improving the performance of deep learning models. These findings have the potential to enable new developments in diabetic retinopathy analysis. The challenge remains open for post-challenge registrations and submissions for benchmarking future methodology developments.

Diego Monteiro, Xian Wang, Hai-Ning Liang et al.

Virtual Reality (VR) head-mounted displays (HMDs) have been studied widely as tools for the most diverse kinds of training activities. One special kind that is the basis for many real-world applications is spatial knowledge acquisition and navigation. For example, knowing well by heart escape routes can be an important factor for firefighters and soldiers. Prior research on how well knowledge acquired in virtual worlds translates to real, physical one has had mixed results, with some suggesting spatial learning in VR is akin to using a regular 2D display. However, VR HMDs have evolved drastically in the last decade, and little is known about how spatial training skills in a simulated environment using up-to-date VR HMDs compares to training in the real world. In this paper, we aim to investigate how people trained in a VR maze compare against those trained in a physical maze in terms of recall of the position of items inside the environment. While our results did not find significant differences in time performance for people who experienced the physical and those who trained in VR, other behavioural factors were different.

Yeli Feng, Yiyu Cai

Recent studies have shown that deep convolutional neural networks (DCNN) are vulnerable to adversarial examples and sensitive to perceptual quality as well as the acquisition condition of images. These findings raise a big concern for the adoption of DCNN-based applications for critical tasks. In the literature, various defense strategies have been introduced to increase the robustness of DCNN, including re-training an entire model with benign noise injection, adversarial examples, or adding extra layers. In this paper, we investigate the connection between adversarial manipulation and image quality, subsequently propose a protective mechanism that doesnt require re-training a DCNN. Our method combines image quality assessment with knowledge distillation to detect input images that would trigger a DCCN to produce egregiously wrong results. Using the ResNet model trained on ImageNet as an example, we demonstrate that the detector can effectively identify poor quality and adversarial images.