Search Results for author: Matthew McDermott

Found 12 papers, 3 papers with code

Using Machine Learning to Develop Smart Reflex Testing Protocols

no code implementations1 Feb 2023 Matthew McDermott, Anand Dighe, Peter Szolovits, Yuan Luo, Jason Baron

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.

Imputation Management

DNN Filter for Bias Reduction in Distribution-to-Distribution Scan Matching

no code implementations8 Nov 2022 Matthew McDermott, Jason Rife

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.

Point Cloud Registration

Mitigating Shadows in Lidar Scan Matching using Spherical Voxels

no code implementations1 Aug 2022 Matthew McDermott, Jason Rife

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

no code implementations29 Jul 2022 Matthew McDermott, Jason Rife

Lidar data can be used to generate point clouds for the navigation of autonomous vehicles or mobile robotics platforms.

Autonomous Vehicles

Meta-Learning to Improve Pre-Training

no code implementations NeurIPS 2021 Aniruddh Raghu, Jonathan Lorraine, Simon Kornblith, Matthew McDermott, David Duvenaud

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.

Data Augmentation Hyperparameter Optimization +1

Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings

1 code implementation11 Mar 2020 Haoran Zhang, Amy X. Lu, Mohamed Abdalla, Matthew McDermott, Marzyeh Ghassemi

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.

Fairness Word Embeddings

CheXclusion: Fairness gaps in deep chest X-ray classifiers

1 code implementation14 Feb 2020 Laleh Seyyed-Kalantari, Guanxiong Liu, Matthew McDermott, Irene Y. Chen, Marzyeh Ghassemi

We demonstrate that TPR disparities exist in the state-of-the-art classifiers in all datasets, for all clinical tasks, and all subgroups.

Fairness Medical Diagnosis +2

A Framework for Relation Extraction Across Multiple Datasets in Multiple Domains

no code implementations WS 2019 Geeticka Chauhan, Matthew McDermott, Peter Szolovits

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.

Relation Extraction

Clinically Accurate Chest X-Ray Report Generation

1 code implementation4 Apr 2019 Guanxiong Liu, Tzu-Ming Harry Hsu, Matthew McDermott, Willie Boag, Wei-Hung Weng, Peter Szolovits, Marzyeh Ghassemi

The automatic generation of radiology reports given medical radiographs has significant potential to operationally and improve clinical patient care.

Text Generation

Unsupervised Multimodal Representation Learning across Medical Images and Reports

no code implementations21 Nov 2018 Tzu-Ming Harry Hsu, Wei-Hung Weng, Willie Boag, Matthew McDermott, Peter Szolovits

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.

Representation Learning Retrieval

Machine Learning for Health (ML4H) Workshop at NeurIPS 2018

no code implementations17 Nov 2018 Natalia Antropova, Andrew L. Beam, Brett K. Beaulieu-Jones, Irene Chen, Corey Chivers, Adrian Dalca, Sam Finlayson, Madalina Fiterau, Jason Alan Fries, Marzyeh Ghassemi, Mike Hughes, Bruno Jedynak, Jasvinder S. Kandola, Matthew McDermott, Tristan Naumann, Peter Schulam, Farah Shamout, Alexandre Yahi

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.

BIG-bench Machine Learning

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