no code implementations • 12 Mar 2024 • Juan Manuel Zambrano Chaves, Shih-Cheng Huang, Yanbo Xu, Hanwen Xu, Naoto Usuyama, Sheng Zhang, Fei Wang, Yujia Xie, Mahmoud Khademi, ZiYi Yang, Hany Awadalla, Julia Gong, Houdong Hu, Jianwei Yang, Chunyuan Li, Jianfeng Gao, Yu Gu, Cliff Wong, Mu Wei, Tristan Naumann, Muhao Chen, Matthew P. Lungren, Serena Yeung-Levy, Curtis P. Langlotz, Sheng Wang, Hoifung Poon
Frontier general-domain models such as GPT-4V still have significant performance gaps in multimodal biomedical applications.
1 code implementation • 14 Sep 2023 • Dave Van Veen, Cara Van Uden, Louis Blankemeier, Jean-Benoit Delbrouck, Asad Aali, Christian Bluethgen, Anuj Pareek, Malgorzata Polacin, Eduardo Pontes Reis, Anna Seehofnerova, Nidhi Rohatgi, Poonam Hosamani, William Collins, Neera Ahuja, Curtis P. Langlotz, Jason Hom, Sergios Gatidis, John Pauly, Akshay S. Chaudhari
Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time.
1 code implementation • 2 May 2023 • Dave Van Veen, Cara Van Uden, Maayane Attias, Anuj Pareek, Christian Bluethgen, Malgorzata Polacin, Wah Chiu, Jean-Benoit Delbrouck, Juan Manuel Zambrano Chaves, Curtis P. Langlotz, Akshay S. Chaudhari, John Pauly
We systematically investigate lightweight strategies to adapt large language models (LLMs) for the task of radiology report summarization (RRS).
1 code implementation • 23 Nov 2022 • Pierre Chambon, Christian Bluethgen, Jean-Benoit Delbrouck, Rogier van der Sluijs, Małgorzata Połacin, Juan Manuel Zambrano Chaves, Tanishq Mathew Abraham, Shivanshu Purohit, Curtis P. Langlotz, Akshay Chaudhari
We present evidence that the resulting model (RoentGen) is able to create visually convincing, diverse synthetic CXR images, and that the output can be controlled to a new extent by using free-form text prompts including radiology-specific language.
1 code implementation • 21 Oct 2022 • Jean-Benoit Delbrouck, Pierre Chambon, Christian Bluethgen, Emily Tsai, Omar Almusa, Curtis P. Langlotz
To overcome this limitation, we propose a new method, the RadGraph reward, to further improve the factual completeness and correctness of generated radiology reports.
no code implementations • 9 Oct 2022 • Pierre Chambon, Christian Bluethgen, Curtis P. Langlotz, Akshay Chaudhari
Multi-modal foundation models are typically trained on millions of pairs of natural images and text captions, frequently obtained through web-crawling approaches.
1 code implementation • 28 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.
no code implementations • 3 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.
no code implementations • 2 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.
3 code implementations • 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.
7 code implementations • 2 Oct 2020 • Yuhao Zhang, Hang Jiang, Yasuhide Miura, Christopher D. Manning, Curtis P. Langlotz
Existing work commonly relies on fine-tuning weights transferred from ImageNet pretraining, which is suboptimal due to drastically different image characteristics, or rule-based label extraction from the textual report data paired with medical images, which is inaccurate and hard to generalize.
1 code implementation • 18 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.
5 code implementations • 29 Jul 2020 • Yuhao Zhang, Yuhui Zhang, Peng Qi, Christopher D. Manning, Curtis P. Langlotz
We introduce biomedical and clinical English model packages for the Stanza Python NLP library.
1 code implementation • Nature 2020 • David Ouyang, Bryan He, Amirata Ghorbani, Neal Yuan, Joseph Ebinger, Curtis P. Langlotz, Paul A. Heidenreich, Robert A. Harrington, David H. Liang, Euan A. Ashley, James Y. Zou
Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease, screening for cardiotoxicity and decisions regarding the clinical management of patients with a critical illness.
Ranked #2 on on Echonet-Dynamic
no code implementations • ACL 2020 • Yuhao Zhang, Derek Merck, Emily Bao Tsai, Christopher D. Manning, Curtis P. Langlotz
Neural abstractive summarization models are able to generate summaries which have high overlap with human references.
no code implementations • 24 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.
12 code implementations • 21 Jan 2019 • Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, Jayne Seekins, David A. Mong, Safwan S. Halabi, Jesse K. Sandberg, Ricky Jones, David B. Larson, Curtis P. Langlotz, Bhavik N. Patel, Matthew P. Lungren, Andrew Y. Ng
On a validation set of 200 chest radiographic studies which were manually annotated by 3 board-certified radiologists, we find that different uncertainty approaches are useful for different pathologies.
Ranked #95 on Multi-Label Classification on CheXpert
1 code implementation • Medicine 2018 • Nicholas Bien, Pranav Rajpurkar, Robyn L. Ball, Jeremy Irvin, Allison Park, Erik Jones, Michael Bereket, Bhavik N. Patel, Kristen W. Yeom, Katie Shpanskaya, Safwan Halabi, Evan Zucker, Gary Fanton, Derek F. Amanatullah, Christopher F. Beaulieu, Geoffrey M. Riley, Russell J. Stewart, Francis G. Blankenberg, David B. Larson, Ricky H. Jones, Curtis P. Langlotz, Andrew Y. Ng, Matthew P. Lungren
Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries.
Ranked #1 on Multi-Label Classification on MRNet
2 code implementations • 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.