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Found 15 papers, 7 papers with code
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
The organization of the research roundtables at the conference involved 17 Senior Chairs and 19 Junior Chairs across 11 tables.
Event-Based Contrastive Learning for Medical Time Series
In clinical practice, one often needs to identify whether a patient is at high risk of adverse outcomes after some key medical event.
ICET Online Accuracy Characterization for Geometry-Based Laser Scan Matching
In this paper we introduce the Iterative Closest Ellipsoidal Transform (ICET), a novel 3D LIDAR scan-matching algorithm that re-envisions NDT in order to provide robust accuracy prediction from first principles.
Using Machine Learning to Develop Smart Reflex Testing Protocols
Here, using the analyte ferritin as an example, we propose an alternative machine learning-based approach to "smart" reflex testing with a wider scope and greater impact than traditional rule-based approaches.
DNN Filter for Bias Reduction in Distribution-to-Distribution Scan Matching
Distribution-to-distribution (D2D) point cloud registration techniques such as the Normal Distributions Transform (NDT) can align point clouds sampled from unstructured scenes and provide accurate bounds of their own solution error covariance -- an important feature for safety-of-life navigation tasks.
Mitigating Shadows in Lidar Scan Matching using Spherical Voxels
In this paper we propose an approach to mitigate shadowing errors in Lidar scan matching, by introducing a preprocessing step based on spherical gridding.
Enhanced Laser-Scan Matching with Online Error Estimation for Highway and Tunnel Driving
Lidar data can be used to generate point clouds for the navigation of autonomous vehicles or mobile robotics platforms.
Meta-Learning to Improve Pre-Training
Pre-training (PT) followed by fine-tuning (FT) is an effective method for training neural networks, and has led to significant performance improvements in many domains.
Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings
In this work, we examine the extent to which embeddings may encode marginalized populations differently, and how this may lead to a perpetuation of biases and worsened performance on clinical tasks.
CheXclusion: Fairness gaps in deep chest X-ray classifiers
We demonstrate that TPR disparities exist in the state-of-the-art classifiers in all datasets, for all clinical tasks, and all subgroups.
Ranked #1 on
Multi-Label Classification
on MIMIC-CXR
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.
Clinically Accurate Chest X-Ray Report Generation
The automatic generation of radiology reports given medical radiographs has significant potential to operationally and improve clinical patient care.
Unsupervised Multimodal Representation Learning across Medical Images and Reports
Joint embeddings between medical imaging modalities and associated radiology reports have the potential to offer significant benefits to the clinical community, ranging from cross-domain retrieval to conditional generation of reports to the broader goals of multimodal representation learning.
Machine Learning for Health (ML4H) Workshop at NeurIPS 2018
This volume represents the accepted submissions from the Machine Learning for Health (ML4H) workshop at the conference on Neural Information Processing Systems (NeurIPS) 2018, held on December 8, 2018 in Montreal, Canada.
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.
