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Found 8 papers, 6 papers with code
Improving Radiology Report Generation Systems by Removing Hallucinated References to Non-existent Priors
Current deep learning models trained to generate radiology reports from chest radiographs are capable of producing clinically accurate, clear, and actionable text that can advance patient care.
Contrastive learning-based pretraining improves representation and transferability of diabetic retinopathy classification models
CL based pretraining with NST significantly improves DL classification performance, helps the model generalize well (transferable from EyePACS to UIC data), and allows training with small, annotated datasets, therefore reducing ground truth annotation burden of the clinicians.
Unsupervised Tokenization Learning
In the presented study, we discover that the so-called "transition freedom" metric appears superior for unsupervised tokenization purposes in comparison to statistical metrics such as mutual information and conditional probability, providing F-measure scores in range from 0. 71 to 1. 0 across explored multilingual corpora.
End-to-end Malaria Diagnosis and 3D Cell Rendering with Deep Learning
Several deaths due to malaria are byproducts of disparities in the social determinants of health; the current gold standard for diagnosing malaria requires microscopes, reagents, and other equipment that most patients of low socioeconomic brackets do not have access to.
COVID-19 Lung Lesion Segmentation Using a Sparsely Supervised Mask R-CNN on Chest X-rays Automatically Computed from Volumetric CTs
Chest X-rays of coronavirus disease 2019 (COVID-19) patients are frequently obtained to determine the extent of lung disease and are a valuable source of data for creating artificial intelligence models.
Natural Language Generation Using Link Grammar for General Conversational Intelligence
Many current artificial general intelligence (AGI) and natural language processing (NLP) architectures do not possess general conversational intelligence--that is, they either do not deal with language or are unable to convey knowledge in a form similar to the human language without manual, labor-intensive methods such as template-based customization.
ClefNet: Recurrent Autoencoders with Dynamic Time Warping for Near-Lossless Music Compression and Minimal-Latency Transmission
The onset of coronavirus disease 2019 (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has sparked unprecedented change.
Interpretable Natural Language Segmentation Based on Link Grammar
Natural language segmentation (NLS), or text segmentation, refers to the process of dividing written text into meaningful units.
