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Found 12 papers, 4 papers with code
Using Multiple Instance Learning to Build Multimodal Representations
Image-text multimodal representation learning aligns data across modalities and enables important medical applications, e. g., image classification, visual grounding, and cross-modal retrieval.
RadTex: Learning Efficient Radiograph Representations from Text Reports
In this paper, we build a data-efficient learning framework that utilizes radiology reports to improve medical image classification performance with limited labeled data (fewer than 1000 examples).
Image Classification with Consistent Supporting Evidence
We define consistent evidence to be both compatible and sufficient with respect to model predictions.
Multimodal Representation Learning via Maximization of Local Mutual Information
We propose and demonstrate a representation learning approach by maximizing the mutual information between local features of images and text.
Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment
To take advantage of the rich information present in the radiology reports, we develop a neural network model that is trained on both images and free-text to assess pulmonary edema severity from chest radiographs at inference time.
Deep Learning to Quantify Pulmonary Edema in Chest Radiographs
Results: The area under the receiver operating characteristic curve (AUC) for differentiating alveolar edema from no edema was 0. 99 for the semi-supervised model and 0. 87 for the pre-trained models.
Fast, Structured Clinical Documentation via Contextual Autocomplete
We present a system that uses a learned autocompletion mechanism to facilitate rapid creation of semi-structured clinical documentation.
Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph
Increasingly large electronic health records (EHRs) provide an opportunity to algorithmically learn medical knowledge.
Semi-supervised Learning for Quantification of Pulmonary Edema in Chest X-Ray Images
We propose and demonstrate machine learning algorithms to assess the severity of pulmonary edema in chest x-ray images of congestive heart failure patients.
MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs
Chest radiography is an extremely powerful imaging modality, allowing for a detailed inspection of a patient's thorax, but requiring specialized training for proper interpretation.
Clinical Tagging with Joint Probabilistic Models
We describe a method for parameter estimation in bipartite probabilistic graphical models for joint prediction of clinical conditions from the electronic medical record.
Anchored Discrete Factor Analysis
We present a semi-supervised learning algorithm for learning discrete factor analysis models with arbitrary structure on the latent variables.
