Fast Window-Based Event Denoising with Spatiotemporal Correlation Enhancement.

Previous deep learning-based event denoising methods mostly suffer from poor interpretability and difficulty in real-time processing due to their complex architecture designs. In this paper, we propose window-based event denoising, which simultaneously deals with a stack of events while existing element-based denoising focuses on one event each time. Besides, we give the theoretical analysis based on probability distributions in both temporal and spatial domains to improve interpretability.

Percutaneous endoscopic gastrostomy prior to esophagectomy for esophageal cancer - a systematic review and meta-analysis.

Background: For resectable esophageal cancer (EC), it remains controversial whether to place percutaneous endoscopic gastrostomy (PEG) before the curative surgery to provide nutritional support during the neoadjuvant therapy.

Objective: To compare surgical outcomes for patients who received preoperative PEG and those without PEG placement (No-PEG) insertion prior to surgery in a potentially operable EC.

Methods: A comprehensive literature search was conducted to identify randomized and non-randomized studies comparing PEG and No-PEG groups.