184 citations · 556 across the 16 of their papers we have counts for
16 papers
GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records
Xi Yang, Aokun Chen, Nima PourNejatian +15
There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powe…
Automatic Extraction of Medication Names in Tweets as Named Entity Recognition
Carol Anderson, Bo Liu, Anas Abidin +2
Social media posts contain potentially valuable information about medical conditions and health-related behavior. Biocreative VII Task 3 focuses on mining this information by recog…
Chemical Identification and Indexing in PubMed Articles via BERT and Text-to-Text Approaches
Virginia Adams, Hoo-Chang Shin, Carol Anderson +2
The Biocreative VII Track-2 challenge consists of named entity recognition, entity-linking (or entity-normalization), and topic indexing tasks -- with entities and topics limited t…
Text Mining Drug/Chemical-Protein Interactions using an Ensemble of BERT and T5 Based Models
Virginia Adams, Hoo-Chang Shin, Carol Anderson +2
In Track-1 of the BioCreative VII Challenge participants are asked to identify interactions between drugs/chemicals and proteins. In-context named entity annotations for each drug/…
BioMegatron: Larger Biomedical Domain Language Model
Hoo-Chang Shin, Yang Zhang, Evelina Bakhturina +4
There has been an influx of biomedical domain-specific language models, showing language models pre-trained on biomedical text perform better on biomedical domain benchmarks than t…
GANDALF: Generative Adversarial Networks with Discriminator-Adaptive Loss Fine-tuning for Alzheimer's Disease Diagnosis from MRI
Hoo-Chang Shin, Alvin Ihsani, Ziyue Xu +5
Positron Emission Tomography (PET) is now regarded as the gold standard for the diagnosis of Alzheimer's Disease (AD). However, PET imaging can be prohibitive in terms of cost and…
GANBERT: Generative Adversarial Networks with Bidirectional Encoder Representations from Transformers for MRI to PET synthesis
Hoo-Chang Shin, Alvin Ihsani, Swetha Mandava +4
Synthesizing medical images, such as PET, is a challenging task due to the fact that the intensity range is much wider and denser than those in photographs and digital renderings a…
Correlation via synthesis: end-to-end nodule image generation and radiogenomic map learning based on generative adversarial network
Ziyue Xu, Xiaosong Wang, Hoo-Chang Shin +5
Radiogenomic map linking image features and gene expression profiles is useful for noninvasively identifying molecular properties of a particular type of disease. Conventionally, s…
Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks
Hoo-Chang Shin, Neil A Tenenholtz, Jameson K Rogers +5
Data diversity is critical to success when training deep learning models. Medical imaging data sets are often imbalanced as pathologic findings are generally rare, which introduces…
Unsupervised Joint Mining of Deep Features and Image Labels for Large-scale Radiology Image Categorization and Scene Recognition
Xiaosong Wang, Le Lu, Hoo-chang Shin +5
The recent rapid and tremendous success of deep convolutional neural networks (CNN) on many challenging computer vision tasks largely derives from the accessibility of the well-ann…
Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation
Hoo-Chang Shin, Kirk Roberts, Le Lu +3
Despite the recent advances in automatically describing image contents, their applications have been mostly limited to image caption datasets containing natural images (e.g., Flick…
Unsupervised Category Discovery via Looped Deep Pseudo-Task Optimization Using a Large Scale Radiology Image Database
Xiaosong Wang, Le Lu, Hoo-chang Shin +4
Obtaining semantic labels on a large scale radiology image database (215,786 key images from 61,845 unique patients) is a prerequisite yet bottleneck to train highly effective deep…
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Hoo-Chang Shin, Holger R. Roth, Mingchen Gao +6
Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and the revival of deep CNN. CNNs enable learning data-d…
DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation
Holger R. Roth, Le Lu, Amal Farag +4
Automatic organ segmentation is an important yet challenging problem for medical image analysis. The pancreas is an abdominal organ with very high anatomical variability. This inhi…
Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database for Automated Image Interpretation
Hoo-Chang Shin, Le Lu, Lauren Kim +3
Despite tremendous progress in computer vision, there has not been an attempt for machine learning on very large-scale medical image databases. We present an interleaved text/image…
Anatomy-specific classification of medical images using deep convolutional nets
Holger R. Roth, Christopher T. Lee, Hoo-Chang Shin +5
Automated classification of human anatomy is an important prerequisite for many computer-aided diagnosis systems. The spatial complexity and variability of anatomy throughout the h…