Assessing Knee OA Severity with CNN attentionbased endtoend architectures This work proposes a novel endtoend convolutional neural network (CNN) architecture to automatically quantify the severity of knee osteoarthritis (OA) using XRay images, which incorporates trainable attention modules acting as unsupervised finegrained detectors of the region of interest (ROI). The proposed attention modules can be applied at different levels and scales across any CNN pipeline helping the network to learn relevant attention patterns over the most informative parts of the image at different resolutions. We test the proposed attention mechanism on existing stateoftheart CNN architectures as our base models, achieving promising results on the benchmark knee OA datasets from the osteoarthritis initiative (OAI) and multicenter osteoarthritis study (MOST).

Convolutional Neural Network and Attention Mechanism for Bone Age Prediction Deep learning has expanded essential role in healthcare system for monitoring and diagnosis support. Rather than trying to make any diagnostic decision, the system just acting as existing medical wisdom accessible. Therefore, this research aims to apply deep learning technique, convolutional neural networks (CNN) for bone age classification as a supporting tool for related fields in bone age diagnosis. Although there are various types of researches have been investigated for bone age assessment with CNN, the Attention mechanism has not been profoundly compared with standardized atlas collection of hand radiography for bone age assessment. Therefore, we propose the investigation of bone age prediction model based on Deep Learning techniques and Attention mechanism for screening tool in bone age evaluation.