Search Results for author: Curtis P. Langlotz

Found 12 papers, 7 papers with code

RadGraph: Extracting Clinical Entities and Relations from Radiology Reports

no code implementations28 Jun 2021 Saahil Jain, Ashwin Agrawal, Adriel Saporta, Steven QH Truong, Du Nguyen Duong, Tan Bui, Pierre Chambon, Yuhao Zhang, Matthew P. Lungren, Andrew Y. Ng, Curtis P. Langlotz, Pranav Rajpurkar

We release a development dataset, which contains board-certified radiologist annotations for 500 radiology reports from the MIMIC-CXR dataset (14, 579 entities and 10, 889 relations), and a test dataset, which contains two independent sets of board-certified radiologist annotations for 100 radiology reports split equally across the MIMIC-CXR and CheXpert datasets.

Relation Extraction

Simulating time to event prediction with spatiotemporal echocardiography deep learning

no code implementations3 Mar 2021 Rohan Shad, Nicolas Quach, Robyn Fong, Patpilai Kasinpila, Cayley Bowles, Kate M. Callon, Michelle C. Li, Jeffrey Teuteberg, John P. Cunningham, Curtis P. Langlotz, William Hiesinger

Integrating methods for time-to-event prediction with diagnostic imaging modalities is of considerable interest, as accurate estimates of survival requires accounting for censoring of individuals within the observation period.

Time-to-Event Prediction

Medical Imaging and Machine Learning

no code implementations2 Mar 2021 Rohan Shad, John P. Cunningham, Euan A. Ashley, Curtis P. Langlotz, William Hiesinger

Advances in computing power, deep learning architectures, and expert labelled datasets have spurred the development of medical imaging artificial intelligence systems that rival clinical experts in a variety of scenarios.

Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation

1 code implementation NAACL 2021 Yasuhide Miura, Yuhao Zhang, Emily Bao Tsai, Curtis P. Langlotz, Dan Jurafsky

We further show via a human evaluation and a qualitative analysis that our system leads to generations that are more factually complete and consistent compared to the baselines.

Natural Language Inference Text Generation

Contrastive Learning of Medical Visual Representations from Paired Images and Text

3 code implementations2 Oct 2020 Yuhao Zhang, Hang Jiang, Yasuhide Miura, Christopher D. Manning, Curtis P. Langlotz

Learning visual representations of medical images is core to medical image understanding but its progress has been held back by the small size of hand-labeled datasets.

Contrastive Learning Image Classification

SCREENet: A Multi-view Deep Convolutional Neural Network for Classification of High-resolution Synthetic Mammographic Screening Scans

1 code implementation18 Sep 2020 Saeed Seyyedi, Margaret J. Wong, Debra M. Ikeda, Curtis P. Langlotz

Purpose: To develop and evaluate the accuracy of a multi-view deep learning approach to the analysis of high-resolution synthetic mammograms from digital breast tomosynthesis screening cases, and to assess the effect on accuracy of image resolution and training set size.

Plexus Convolutional Neural Network (PlexusNet): A novel neural network architecture for histologic image analysis

no code implementations24 Aug 2019 Okyaz Eminaga, Mahmoud Abbas, Christian Kunder, Andreas M. Loening, Jeanne Shen, James D. Brooks, Curtis P. Langlotz, Daniel L. Rubin

A well-fitted PlexusNet-based model delivered comparable classification performance (AUC: 0. 963) in distinguishing prostate cancer from healthy tissues, although it was at least 23 times smaller, had a better model calibration and clinical utility than the comparison models.

General Classification

Learning to Summarize Radiology Findings

1 code implementation WS 2018 Yuhao Zhang, Daisy Yi Ding, Tianpei Qian, Christopher D. Manning, Curtis P. Langlotz

The Impression section of a radiology report summarizes crucial radiology findings in natural language and plays a central role in communicating these findings to physicians.

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