Search Results for author: Curtis Langlotz

Found 17 papers, 8 papers with code

Overview of the MEDIQA 2021 Shared Task on Summarization in the Medical Domain

no code implementations NAACL (BioNLP) 2021 Asma Ben Abacha, Yassine Mrabet, Yuhao Zhang, Chaitanya Shivade, Curtis Langlotz, Dina Demner-Fushman

The MEDIQA 2021 shared tasks at the BioNLP 2021 workshop addressed three tasks on summarization for medical text: (i) a question summarization task aimed at exploring new approaches to understanding complex real-world consumer health queries, (ii) a multi-answer summarization task that targeted aggregation of multiple relevant answers to a biomedical question into one concise and relevant answer, and (iii) a radiology report summarization task addressing the development of clinically relevant impressions from radiology report findings.

Text Summarization

LieRE: Generalizing Rotary Position Encodings

no code implementations14 Jun 2024 Sophie Ostmeier, Brian Axelrod, Michael E. Moseley, Akshay Chaudhari, Curtis Langlotz

While Rotary Position Embeddings (RoPE) for natural language performs well and has become widely adopted, its adoption for other modalities has been slower.

Image Classification Position

GREEN: Generative Radiology Report Evaluation and Error Notation

no code implementations6 May 2024 Sophie Ostmeier, Justin Xu, Zhihong Chen, Maya Varma, Louis Blankemeier, Christian Bluethgen, Arne Edward Michalson, Michael Moseley, Curtis Langlotz, Akshay S Chaudhari, Jean-Benoit Delbrouck

Evaluating radiology reports is a challenging problem as factual correctness is extremely important due to the need for accurate medical communication about medical images.

Natural Language Understanding

Auto-Generating Weak Labels for Real & Synthetic Data to Improve Label-Scarce Medical Image Segmentation

1 code implementation25 Apr 2024 Tanvi Deshpande, Eva Prakash, Elsie Gyang Ross, Curtis Langlotz, Andrew Ng, Jeya Maria Jose Valanarasu

The high cost of creating pixel-by-pixel gold-standard labels, limited expert availability, and presence of diverse tasks make it challenging to generate segmentation labels to train deep learning models for medical imaging tasks.

Image Segmentation Medical Image Segmentation +1

CheXagent: Towards a Foundation Model for Chest X-Ray Interpretation

no code implementations22 Jan 2024 Zhihong Chen, Maya Varma, Jean-Benoit Delbrouck, Magdalini Paschali, Louis Blankemeier, Dave Van Veen, Jeya Maria Jose Valanarasu, Alaa Youssef, Joseph Paul Cohen, Eduardo Pontes Reis, Emily B. Tsai, Andrew Johnston, Cameron Olsen, Tanishq Mathew Abraham, Sergios Gatidis, Akshay S. Chaudhari, Curtis Langlotz

However, developing FMs that can accurately interpret CXRs is challenging due to the (1) limited availability of large-scale vision-language datasets in the medical image domain, (2) lack of vision and language encoders that can capture the complexities of medical data, and (3) absence of evaluation frameworks for benchmarking the abilities of FMs on CXR interpretation.

Benchmarking Fairness +2

ViLLA: Fine-Grained Vision-Language Representation Learning from Real-World Data

1 code implementation ICCV 2023 Maya Varma, Jean-Benoit Delbrouck, Sarah Hooper, Akshay Chaudhari, Curtis Langlotz

The first key contribution of this work is to demonstrate through systematic evaluations that as the pairwise complexity of the training dataset increases, standard VLMs struggle to learn region-attribute relationships, exhibiting performance degradations of up to 37% on retrieval tasks.

Attribute object-detection +3

How to Train Your CheXDragon: Training Chest X-Ray Models for Transfer to Novel Tasks and Healthcare Systems

no code implementations13 May 2023 Cara Van Uden, Jeremy Irvin, Mars Huang, Nathan Dean, Jason Carr, Andrew Ng, Curtis Langlotz

In addition, we experiment with different transfer learning strategies to effectively adapt these pretrained models to new tasks and healthcare systems.

Self-Supervised Learning Transfer Learning

Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays

1 code implementation30 Jan 2023 Rogier van der Sluijs, Nandita Bhaskhar, Daniel Rubin, Curtis Langlotz, Akshay Chaudhari

Thus, it is unknown whether common augmentation strategies employed in Siamese representation learning generalize to medical images and to what extent.

Anomaly Detection Representation Learning +1

Toward expanding the scope of radiology report summarization to multiple anatomies and modalities

1 code implementation15 Nov 2022 Zhihong Chen, Maya Varma, Xiang Wan, Curtis Langlotz, Jean-Benoit Delbrouck

We then conduct extensive experiments to evaluate the performance of models both within and across modality-anatomy pairs in MIMIC-RRS.


Cut out the annotator, keep the cutout: better segmentation with weak supervision

no code implementations ICLR 2021 Sarah Hooper, Michael Wornow, Ying Hang Seah, Peter Kellman, Hui Xue, Frederic Sala, Curtis Langlotz, Christopher Re

We propose a framework that fuses limited label learning and weak supervision for segmentation tasks, enabling users to train high-performing segmentation CNNs with very few hand-labeled training points.

Data Augmentation Few-Shot Learning +4

Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning

2 code implementations30 Jan 2018 Xuan Wang, Yu Zhang, Xiang Ren, Yuhao Zhang, Marinka Zitnik, Jingbo Shang, Curtis Langlotz, Jiawei Han

Motivation: State-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and diseases.

Feature Engineering Multi-Task Learning +4

MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs

11 code implementations11 Dec 2017 Pranav Rajpurkar, Jeremy Irvin, Aarti Bagul, Daisy Ding, Tony Duan, Hershel Mehta, Brandon Yang, Kaylie Zhu, Dillon Laird, Robyn L. Ball, Curtis Langlotz, Katie Shpanskaya, Matthew P. Lungren, Andrew Y. Ng

To evaluate models robustly and to get an estimate of radiologist performance, we collect additional labels from six board-certified Stanford radiologists on the test set, consisting of 207 musculoskeletal studies.

Anomaly Detection Specificity

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