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Found 9 papers, 1 papers with code
OncoNet: Weakly Supervised Siamese Network to automate cancer treatment response assessment between longitudinal FDG PET/CT examinations
FDG PET/CT imaging is a resource intensive examination critical for managing malignant disease and is particularly important for longitudinal assessment during therapy.
CheXternal: Generalization of Deep Learning Models for Chest X-ray Interpretation to Photos of Chest X-rays and External Clinical Settings
Recent advances in training deep learning models have demonstrated the potential to provide accurate chest X-ray interpretation and increase access to radiology expertise.
CheXphotogenic: Generalization of Deep Learning Models for Chest X-ray Interpretation to Photos of Chest X-rays
In this study, we measured the diagnostic performance for 8 different chest x-ray models when applied to photos of chest x-rays.
Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures.
Understanding a medical conversation between a patient and a physician poses unique natural language understanding challenge since it combines elements of standard open-ended conversation with very domain-specific elements that require expertise and medical knowledge.
GloFlow: Global Image Alignment for Creation of Whole Slide Images for Pathology from Video
The application of deep learning to pathology assumes the existence of digital whole slide images of pathology slides.
Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures
Understanding a medical conversation between a patient and a physician poses a unique natural language understanding challenge since it combines elements of standard open ended conversation with very domain specific elements that require expertise and medical knowledge.
CheXpedition: Investigating Generalization Challenges for Translation of Chest X-Ray Algorithms to the Clinical Setting
First, we find that the top 10 chest x-ray models on the CheXpert competition achieve an average AUC of 0. 851 on the task of detecting TB on two public TB datasets without fine-tuning or including the TB labels in training data.
Reevaluating Argument Component Extraction in Low Resource Settings
Argument component extraction is a challenging and complex high-level semantic extraction task.
UniMelb at SemEval-2018 Task 12: Generative Implication using LSTMs, Siamese Networks and Semantic Representations with Synonym Fuzzing
This paper describes a warrant classification system for SemEval 2018 Task 12, that attempts to learn semantic representations of reasons, claims and warrants.
