• First challenge to focus on pre-treatment stratification for diabetic macular edema (DME): This study pioneers the use of pre-treatment OCT biomarkers to predict individual responses to anti-VEGF therapy, advancing the concept of personalized medicine in DME management. • Large-scale, publicly accessible OCT dataset: The competition provides one of the most comprehensive open-access DME datasets to date, comprising tens of thousands of OCT images from 2,000 patients, significantly addressing the field’s data scarcity. The dataset includes both per-eye and per-scan annotations for several critical retinal biomarkers, enabling a wide range of supervised learning applications. • Benchmark for future research: With 170 registered teams and 41 finalists, the challenge fostered broad engagement. The best team achieved an AUC of 80.06%, highlighting the feasibility of accurate outcome prediction. The challenge provides standardized evaluation metrics and a curated leaderboard, laying the groundwork for reproducible and comparable AI model development in ophthalmic treatment prediction. Diabetic macular edema (DME) significantly contributes to visual impairment in diabetic patients. Treatment responses to intravitreal therapies vary, highlighting the need for patient stratification to predict therapeutic benefits and enable personalized strategies. To our knowledge, this study is the first to explore pre-treatment stratification for predicting DME treatment responses. To advance this research, we organized the 2nd Asia-Pacific Tele-Ophthalmology Society (APTOS) Big Data Competition in 2021. The competition focused on improving predictive accuracy for anti-VEGF therapy responses using ophthalmic OCT images. We provided a dataset containing tens of thousands of OCT images from 2,000 patients with labels across four sub-tasks. This paper details the competition’s structure, dataset, leading methods, and evaluation metrics. The competition attracted strong scientific community participation, with 170 teams initially registering and 41 reaching the final round. The top-performing team achieved an AUC of 80.06%, highlighting the potential of AI in personalized DME treatment and clinical decision-making.

We propose ***self-diffusion***, a novel framework for solving inverse problems without relying on pretrained generative models. Traditional diffusion-based approaches require training a model on a clean dataset to learn to reverse the forward noising process. This model is then used to sample clean solutions---corresponding to posterior sampling from a Bayesian perspective---that are consistent with the observed data under a specific task. In contrast, self-diffusion introduces a self-consistent iterative process that alternates between noising and denoising steps to progressively refine its estimate of the solution. At each step of self-diffusion, noise is added to the current estimate, and a self-denoiser, which is a single untrained convolutional network randomly initialized from scratch, is continuously trained for certain iterations via a data fidelity loss to predict the solution from the noisy estimate. Essentially, self-diffusion exploits the spectral bias of neural networks and modulates it through a scheduled noise process. Without relying on pretrained score functions or external denoisers, this approach still remains adaptive to arbitrary forward operators and noisy observations, making it highly flexible and broadly applicable. We demonstrate the effectiveness of our approach on a variety of linear inverse problems, showing that self-diffusion achieves competitive or superior performance compared to other methods.

Magnetic resonance imaging (MRI) is powerful in medical diagnostics, yet high-field MRI, despite offering superior image quality, incurs significant costs for procurement, installation, maintenance, and operation, restricting its availability and accessibility, especially in low- and middle-income countries. Addressing this, our study proposes an unsupervised learning algorithm based on cycle-consistent generative adversarial networks. This framework transforms 0.3T low-field MRI into higher-quality 3T-like images, bypassing the need for paired low/high-field training data. The proposed architecture integrates two novel modules to enhance reconstruction quality: (1) an attention block that dynamically balances high-field-like features with the original low-field input, and (2) an edge block that refines boundary details, providing more accurate structural reconstruction. The proposed generative model is trained on large-scale, unpaired, public datasets, and further validated on paired low/high-field acquisitions of three major clinical MRI sequences: T1-weighted, T2-weighted, and fluid-attenuated inversion recovery (FLAIR) imaging. It demonstrates notable improvements in tissue contrast and signal-to-noise ratio while preserving anatomical fidelity. This approach utilizes rich information from publicly available MRI resources, providing a data-efficient unsupervised alternative that complements supervised methods to enhance the utility of low-field MRI.

Cascading failures represent a significant issue in production lines, as they can lead to process defects and safety incidents. An accurate risk assessment of cascading failures is crucial for ensuring both safety and operational efficiency. However, existing methods for assessing cascading failures typically focus only on exposed failures, neglecting hidden failures. Hidden failures are functional faults not apparent under normal operating conditions; they often remain undetected until triggered by another failure event. Considering solely exposed failures thus provides an incomplete picture, insufficient for accurately assessing cascading failure risks. To address this limitation, this article proposes a novel virtual node-based framework designed to assess cascading failure risks explicitly accounting for hidden failures. A Bayesian network approach, enhanced by leveraging connectivity information, is employed to effectively model the structure of the production line. Within this Bayesian network, a virtual node is integrated, thus representing the background impact of hidden failures. Specifically, the interactions between this virtual node and other network nodes explicitly capture the dynamics and mechanisms underlying hidden failures. Building upon this framework, we propose the virtual node-assisted inverse PageRank algorithm. The algorithm is rigorously defined, with mathematically guaranteed properties including positivity, convergence, and an analytical solution. The methodology is validated using a real-world case study involving an aerospace impeller production line. Experimental results demonstrate that the proposed algorithm successfully identifies hidden failures, delivering superior performance compared to traditional risk assessment approaches.

Magnetic resonance imaging (MRI) is a widely used non-invasive imaging modality. However, a persistent challenge lies in balancing image quality with imaging speed. This trade-off is primarily constrained by k-space measurements, which traverse specific trajectories in the spatial Fourier domain (k-space). These measurements are often undersampled to shorten acquisition times, resulting in image artifacts and compromised quality. Generative models learn image distributions and can be used to reconstruct high-quality images from undersampled k-space data. In this work, we present the autoregressive image diffusion (AID) model for image sequences and use it to sample the posterior for accelerated MRI reconstruction. The algorithm incorporates both undersampled k-space and pre-existing information. Models trained with fastMRI dataset are evaluated comprehensively. The results show that the AID model can robustly generate sequentially coherent image sequences. In MRI applications, the AID can outperform the standard diffusion model and reduce hallucinations, due to the learned inter-image dependencies. The project code is available at https://github.com/mrirecon/aid.