To improve diagnostic accuracy, we propose a CXR report generator that integrates aspects of the radiologist workflow and is trained with our proposed reward for reinforcement learning.
Our experimental investigation demonstrates that the Convolutional vision Transformer (CvT) ImageNet-21K and the Distilled Generative Pre-trained Transformer 2 (DistilGPT2) checkpoints are best for warm starting the encoder and decoder, respectively.
This DE measure was an extension of Perceptual Evaluation of Audio Quality, and required reference and test signals.
Motivated by this, we propose the residual-dense lattice network (RDL-Net), which is a new CNN for speech enhancement that employs both residual and dense aggregations without over-allocating parameters for feature re-usage.
Ranked #15 on Speech Enhancement on VoiceBank + DEMAND
Deep learning has achieved substantial improvement on single-channel speech enhancement tasks.
MMSE approaches utilising the proposed a priori SNR estimator are able to achieve higher enhanced speech quality and intelligibility scores than recent masking- and mapping-based deep learning approaches.