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Found 8 papers, 0 papers with code
Generating Mammography Reports from Multi-view Mammograms with BERT
Writing mammography reports can be error-prone and time-consuming for radiologists.
BASE TTS: Lessons from building a billion-parameter Text-to-Speech model on 100K hours of data
Echoing the widely-reported "emergent abilities" of large language models when trained on increasing volume of data, we show that BASE TTS variants built with 10K+ hours and 500M+ parameters begin to demonstrate natural prosody on textually complex sentences.
Controllable Emphasis with zero data for text-to-speech
We show that this technique significantly closes the gap to methods that require explicit recordings.
Whole-body PET image denoising for reduced acquisition time
This paper evaluates the performance of supervised and unsupervised deep learning models for denoising positron emission tomography (PET) images in the presence of reduced acquisition times.
Distribution augmentation for low-resource expressive text-to-speech
This paper presents a novel data augmentation technique for text-to-speech (TTS), that allows to generate new (text, audio) training examples without requiring any additional data.
CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning
Analysis of chest CT scans can be used in detecting parts of lungs that are affected by infectious diseases such as COVID-19. Determining the volume of lungs affected by lesions is essential for formulating treatment recommendations and prioritizingpatients by severity of the disease.
Noise-Resilient Automatic Interpretation of Holter ECG Recordings
Holter monitoring, a long-term ECG recording (24-hours and more), contains a large amount of valuable diagnostic information about the patient.
