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Found 22 papers, 7 papers with code
Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT
The extraction of labels from radiology text reports enables large-scale training of medical imaging models.
Unlocking Robust Segmentation Across All Age Groups via Continual Learning
Most deep learning models in medical imaging are trained on adult data with unclear performance on pediatric images.
LymphoML: An interpretable artificial intelligence-based method identifies morphologic features that correlate with lymphoma subtype
The accurate classification of lymphoma subtypes using hematoxylin and eosin (H&E)-stained tissue is complicated by the wide range of morphological features these cancers can exhibit.
How to Train Your CheXDragon: Training Chest X-Ray Models for Transfer to Novel Tasks and Healthcare Systems
In addition, we experiment with different transfer learning strategies to effectively adapt these pretrained models to new tasks and healthcare systems.
DataPerf: Benchmarks for Data-Centric AI Development
Machine learning research has long focused on models rather than datasets, and prominent datasets are used for common ML tasks without regard to the breadth, difficulty, and faithfulness of the underlying problems.
Effective Data Fusion with Generalized Vegetation Index: Evidence from Land Cover Segmentation in Agriculture
How can we effectively leverage the domain knowledge from remote sensing to better segment agriculture land cover from satellite images?
The 1st Agriculture-Vision Challenge: Methods and Results
The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset.
Neural Text Style Transfer via Denoising and Reranking
We introduce a simple method for text style transfer that frames style transfer as denoising: we synthesize a noisy corpus and treat the source style as a noisy version of the target style.
Predicting Inpatient Discharge Prioritization With Electronic Health Records
Identifying patients who will be discharged within 24 hours can improve hospital resource management and quality of care.
Countdown Regression: Sharp and Calibrated Survival Predictions
Probabilistic survival predictions from models trained with Maximum Likelihood Estimation (MLE) can have high, and sometimes unacceptably high variance.
Noising and Denoising Natural Language: Diverse Backtranslation for Grammar Correction
Translation-based methods for grammar correction that directly map noisy, ungrammatical text to their clean counterparts are able to correct a broad range of errors; however, such techniques are bottlenecked by the need for a large parallel corpus of noisy and clean sentence pairs.
Improving Palliative Care with Deep Learning
The algorithm is a Deep Neural Network trained on the EHR data from previous years, to predict all-cause 3-12 month mortality of patients as a proxy for patients that could benefit from palliative care.
Deep Voice: Real-time Neural Text-to-Speech
We present Deep Voice, a production-quality text-to-speech system constructed entirely from deep neural networks.
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech--two vastly different languages.
Driverseat: Crowdstrapping Learning Tasks for Autonomous Driving
While emerging deep-learning systems have outclassed knowledge-based approaches in many tasks, their application to detection tasks for autonomous technologies remains an open field for scientific exploration.
Deep learning for class-generic object detection
We investigate the use of deep neural networks for the novel task of class generic object detection.
Reasoning With Neural Tensor Networks for Knowledge Base Completion
We assess the model by considering the problem of predicting additional true relations between entities given a partial knowledge base.
Tuned Models of Peer Assessment in MOOCs
In massive open online courses (MOOCs), peer grading serves as a critical tool for scaling the grading of complex, open-ended assignments to courses with tens or hundreds of thousands of students.
Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (2009)
This is the Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, which was held in Montreal, QC, Canada, June 18 - 21 2009.
