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Found 11 papers, 4 papers with code
Bidirectional Captioning for Clinically Accurate and Interpretable Models
Vision-language pretraining has been shown to produce high-quality visual encoders which transfer efficiently to downstream computer vision tasks.
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).
Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View
In this review, we focus on the interpretability of the DL models in healthcare.
How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact
We lay the foundations via the moral philosophy definition of social good, propose a framework to evaluate the direct and indirect real-world impact of NLP tasks, and adopt the methodology of global priorities research to identify priority causes for NLP research.
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.
A Framework for Relation Extraction Across Multiple Datasets in Multiple Domains
Our framework will be open-sourced and will aid in performing systematic exploration on the effect of different modeling techniques, pre-processing, training methodologies and evaluation metrics on the 3 datasets to help establish a consensus.
MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III
Robust machine learning relies on access to data that can be used with standardized frameworks in important tasks and the ability to develop models whose performance can be reasonably reproduced.
Ranked #3 on
Length-of-Stay prediction
on MIMIC-III
REflex: Flexible Framework for Relation Extraction in Multiple Domains
Systematic comparison of methods for relation extraction (RE) is difficult because many experiments in the field are not described precisely enough to be completely reproducible and many papers fail to report ablation studies that would highlight the relative contributions of their various combined techniques.
Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation
Machine learning for healthcare often trains models on de-identified datasets with randomly-shifted calendar dates, ignoring the fact that data were generated under hospital operation practices that change over time.
A New Approach to Animacy Detection
The system achieves an F1 of 0. 88 for classifying the animacy of referring expressions, which is comparable to state of the art results for classifying the animacy of words, and achieves an F1 of 0. 75 for classifying the animacy of coreference chains themselves.
MIT-MEDG at SemEval-2018 Task 7: Semantic Relation Classification via Convolution Neural Network
SemEval 2018 Task 7 tasked participants to build a system to classify two entities within a sentence into one of the 6 possible relation types.
