Make the Most Out of Your Net: Alternating Between Canonical and Hard Datasets for Improved Image Demosaicing

Image demosaicing is an important step in the image processing pipeline for digital cameras, and it is one of the many tasks within the field of image restoration. A well-known characteristic of natural images is that most patches are smooth, while high-content patches like textures or repetitive patterns are much rarer, which results in a long-tailed distribution. This distribution can create an inductive bias when training machine learning algorithms for image restoration tasks and for image demosaicing in particular. There have been many different approaches to address this challenge, such as utilizing specific losses or designing special network architectures. What makes our work is unique in that it tackles the problem from a training protocol perspective. Our proposed training regime consists of two key steps. The first step is a data-mining stage where sub-categories are created and then refined through an elimination process to only retain the most helpful sub-categories. The second step is a cyclic training process where the neural network is trained on both the mined sub-categories and the original dataset. We have conducted various experiments to demonstrate the effectiveness of our training method for the image demosaicing task. Our results show that this method outperforms standard training across a range of architecture sizes and types, including CNNs and Transformers. Moreover, we are able to achieve state-of-the-art results with a significantly smaller neural network, compared to previous state-of-the-art methods

The impact of postoperative aspirin in patients undergoing Woven EndoBridge: a multicenter, institutional, propensity score-matched analysis.

The Woven EndoBridge (WEB) device is frequently used for the treatment of intracranial aneurysms. Postoperative management, including the use of aspirin, varies among clinicians and institutions, but its impact on the outcomes of the WEB has not been thoroughly investigated. This was a retrospective, multicenter study involving 30 academic institutions in North America, South America, and Europe. Data from 1492 patients treated with the WEB device were included. Patients were categorized into two groups based on their postoperative use of aspirin (aspirin group: n=1124, non-aspirin group: n=368). Data points included patient demographics, aneurysm characteristics, procedural details, complications, and angiographic and functional outcomes. Propensity score matching (PSM) was applied to balance variables between the two groups. Prior to PSM, the aspirin group exhibited significantly higher rates of modified Rankin scale (mRS) mRS 0-1 and mRS 0-2 (89.8% vs 73.4% and 94.1% vs 79.8%, p<0.001), lower rates of mortality (1.6% vs 8.6%, p<0.001), and higher major compaction rates (13.4% vs 7%, p<0.001). Post-PSM, the aspirin group showed significantly higher rates of retreatment (p=0.026) and major compaction (p=0.037) while maintaining its higher rates of good functional outcomes and lower mortality rates. In the multivariable regression, aspirin was associated with higher rates of mRS 0-1 (OR 2.166; 95% CI 1.16 to 4, p=0.016) and mRS 0-2 (OR 2.817; 95% CI 1.36 to 5.88, p=0.005) and lower rates of mortality (OR 0.228; 95% CI 0.06 to 0.83, p=0.025). However, it was associated with higher rates of retreatment (OR 2.471; 95% CI 1.11 to 5.51, p=0.027). Aspirin use post-WEB treatment may lead to better functional outcomes and lower mortality but with higher retreatment rates. These insights are crucial for postoperative management after WEB procedures, but further studies are necessary for validation