Publications by authors named "Aya Tonouchi"
Article Synopsis
- Long-term monitoring of leaf pigments is challenging with traditional methods, leading to the development of PlantServation, which uses advanced imaging and deep learning to analyze leaf color non-destructively.
- In a case study with four Arabidopsis species, the method processed over 4 million images, revealing how different environmental factors like sunlight and precipitation impacted anthocyanin levels.
- The findings support the idea that allopolyploids can combine traits from their ancestors, giving insight into how plants adapt in natural, complex environments.
We developed a computer-aided detection (CADe) system to detect and localize colorectal lesions by modifying You-Only-Look-Once version 3 (YOLO v3) and evaluated its performance in two different settings. The test dataset was obtained from 20 randomly selected patients who underwent endoscopic resection for 69 colorectal lesions at the Jikei University Hospital between June 2017 and February 2018. First, we evaluated the diagnostic performances using still images randomly and automatically extracted from video recordings of the entire endoscopic procedure at intervals of 5 s, without eliminating poor quality images.
View Article and Find Full Text PDFBackground: We have developed the computer-aided detection (CADe) system using an original deep learning algorithm based on a convolutional neural network for assisting endoscopists in detecting colorectal lesions during colonoscopy. The aim of this study was to clarify whether adenoma miss rate (AMR) could be reduced with CADe assistance during screening and surveillance colonoscopy.
Methods: This study was a multicenter randomized controlled trial.