no code implementations • 30 Nov 2021 • Virginia Adams, Hoo-chang Shin, Carol Anderson, Bo Liu, Anas Abidin
In Track-1 of the BioCreative VII Challenge participants are asked to identify interactions between drugs/chemicals and proteins.
no code implementations • 30 Nov 2021 • Carol Anderson, Bo Liu, Anas Abidin, Hoo-chang Shin, Virginia Adams
Social media posts contain potentially valuable information about medical conditions and health-related behavior.
no code implementations • 30 Nov 2021 • Virginia Adams, Hoo-chang Shin, Carol Anderson, Bo Liu, Anas Abidin
We extend our BERT-based approach to the entity linking task.
1 code implementation • EMNLP 2020 • Hoo-chang Shin, Yang Zhang, Evelina Bakhturina, Raul Puri, Mostofa Patwary, Mohammad Shoeybi, Raghav Mani
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 those trained on general domain text corpora such as Wikipedia and Books.
Ranked #1 on Named Entity Recognition (NER) on BC5CDR-disease
no code implementations • 10 Aug 2020 • Hoo-chang Shin, Alvin Ihsani, Swetha Mandava, Sharath Turuvekere Sreenivas, Christopher Forster, Jiook Cha, Alzheimer's Disease Neuroimaging Initiative
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 and are often heavily biased toward zero.
no code implementations • 10 Aug 2020 • Hoo-chang Shin, Alvin Ihsani, Ziyue Xu, Swetha Mandava, Sharath Turuvekere Sreenivas, Christopher Forster, Jiook Cha, Alzheimer's Disease Neuroimaging Initiative
This paper proposes an alternative approach to the aforementioned, where AD diagnosis is incorporated in the GAN training objective to achieve the best AD classification performance.
no code implementations • MIDL 2019 • Ziyue Xu, Xiaosong Wang, Hoo-chang Shin, Dong Yang, Holger Roth, Fausto Milletari, Ling Zhang, Daguang Xu
In this work, we investigate the potential of an end-to-end method fusing gene code with image features to generate synthetic pathology image and learn radiogenomic map simultaneously.
no code implementations • 8 Jul 2019 • Ziyue Xu, Xiaosong Wang, Hoo-chang Shin, Dong Yang, Holger Roth, Fausto Milletari, Ling Zhang, Daguang Xu
Radiogenomic map linking image features and gene expression profiles is useful for noninvasively identifying molecular properties of a particular type of disease.
no code implementations • 26 Jul 2018 • Hoo-chang Shin, Neil A. Tenenholtz, Jameson K Rogers, Christopher G Schwarz, Matthew L Senjem, Jeffrey L Gunter, Katherine Andriole, Mark Michalski
Medical imaging data sets are often imbalanced as pathologic findings are generally rare, which introduces significant challenges when training deep learning models.
no code implementations • 23 Jan 2017 • Xiaosong Wang, Le Lu, Hoo-chang Shin, Lauren Kim, Mohammadhadi Bagheri, Isabella Nogues, Jianhua Yao, Ronald M. Summers
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-annotated ImageNet and PASCAL VOC datasets.
1 code implementation • CVPR 2016 • Hoo-chang Shin, Kirk Roberts, Le Lu, Dina Demner-Fushman, Jianhua Yao, Ronald M. Summers
Recurrent neural networks (RNNs) are then trained to describe the contexts of a detected disease, based on the deep CNN features.
no code implementations • 25 Mar 2016 • Xiaosong Wang, Le Lu, Hoo-chang Shin, Lauren Kim, Isabella Nogues, Jianhua Yao, Ronald Summers
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 convolutional neural network (CNN) models for image recognition.
no code implementations • 10 Feb 2016 • Hoo-chang Shin, Holger R. Roth, Mingchen Gao, Le Lu, Ziyue Xu, Isabella Nogues, Jianhua Yao, Daniel Mollura, Ronald M. Summers
Another effective method is transfer learning, i. e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks.
no code implementations • 22 Jun 2015 • Holger R. Roth, Le Lu, Amal Farag, Hoo-chang Shin, Jiamin Liu, Evrim Turkbey, Ronald M. Summers
We propose and evaluate several variations of deep ConvNets in the context of hierarchical, coarse-to-fine classification on image patches and regions, i. e. superpixels.
no code implementations • CVPR 2015 • Hoo-chang Shin, Le Lu, Lauren Kim, Ari Seff, Jianhua Yao, Ronald M. Summers
We present an interleaved text/image deep learning system to extract and mine the semantic interactions of radiology images and reports from a national research hospital's picture archiving and communication system.
no code implementations • 4 May 2015 • Hoo-chang Shin, Le Lu, Lauren Kim, Ari Seff, Jianhua Yao, Ronald M. Summers
We present an interleaved text/image deep learning system to extract and mine the semantic interactions of radiology images and reports from a national research hospital's Picture Archiving and Communication System.
1 code implementation • 15 Apr 2015 • Holger R. Roth, Christopher T. Lee, Hoo-chang Shin, Ari Seff, Lauren Kim, Jianhua Yao, Le Lu, Ronald M. Summers
We show that a data augmentation approach can help to enrich the data set and improve classification performance.