Search Results for author:
Found 11 papers, 7 papers with code
Interoperable synthetic health data with SyntHIR to enable the development of CDSS tools
We demonstrate the development of the tool on the SyntHIR system and then lift it to the Open DIPS environment.
More efficient manual review of automatically transcribed tabular data
We allocated those 90, 000 codes to human reviewers, who used our annotation tool to review the codes.
Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review
In this scoping review, we identified the publicly available datasets of breast H&E stained whole-slide images (WSI) that can be used to develop deep learning algorithms.
Phonocardiogram Classification Using 1-Dimensional Inception Time Convolutional Neural Networks
These sounds are detected by auscultating the heart using a stethoscope, or more recently by a phonocardiogram (PCG).
Ranked #1 on
Predict clinical outcome
on CirCor DigiScope
A Pragmatic Machine Learning Approach to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images
Our approach is to transfer an open source machine learning method for segmentation and classification of nuclei in H&E slides trained on public data to TIL quantification without manual labeling of our data.
Lessons learned developing and using a machine learning model to automatically transcribe 2.3 million handwritten occupation codes
We believe our approach and lessons learned may be useful for other transcription projects that plan to use machine learning in production.
Interactive exploration of population scale pharmacoepidemiology datasets
We have therefore created a tool for interactive exploration of patterns in prescription datasets with millions of samples.
Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
We have attempted to reproduce the results in Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, published in JAMA 2016; 316(22), using publicly available data sets.
Ranked #2 on
Diabetic Retinopathy Grading
on Kaggle EyePACS
Convolutional neural network for breathing phase detection in lung sounds
We evaluated the performance of the method using two methods.
Replication study: Development and validation of deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
We have attempted to replicate the main method in 'Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs' published in JAMA 2016; 316(22).
Diabetic Retinopathy Detection
Medical Image Segmentation
+1
Feature Extraction for Machine Learning Based Crackle Detection in Lung Sounds from a Health Survey
We propose a machine learning based approach for detecting crackles in lung sounds recorded using a stethoscope in a large health survey.
