Multistep Automated Data Labelling Procedure (MADLaP) for Thyroid Nodules on Ultrasound: An Artificial Intelligence Approach for Automating Image Annotation

06/28/2022
by   Jikai Zhang, et al.
0

Machine learning (ML) for diagnosis of thyroid nodules on ultrasound is an active area of research. However, ML tools require large, well-labelled datasets, the curation of which is time-consuming and labor-intensive. The purpose of our study was to develop and test a deep-learning-based tool to facilitate and automate the data annotation process for thyroid nodules; we named our tool Multistep Automated Data Labelling Procedure (MADLaP). MADLaP was designed to take multiple inputs included pathology reports, ultrasound images, and radiology reports. Using multiple step-wise modules including rule-based natural language processing, deep-learning-based imaging segmentation, and optical character recognition, MADLaP automatically identified images of a specific thyroid nodule and correctly assigned a pathology label. The model was developed using a training set of 378 patients across our health system and tested on a separate set of 93 patients. Ground truths for both sets were selected by an experienced radiologist. Performance metrics including yield (how many labeled images the model produced) and accuracy (percentage correct) were measured using the test set. MADLaP achieved a yield of 63 input data moved through each module, while accuracy peaked part way through. Error analysis showed that inputs from certain examination sites had lower accuracy (40 curated datasets of labeled ultrasound images of thyroid nodules. While accurate, the relatively suboptimal yield of MADLaP exposed some challenges when trying to automatically label radiology images from heterogeneous sources. The complex task of image curation and annotation could be automated, allowing for enrichment of larger datasets for use in machine learning development.

READ FULL TEXT

page 14

page 15

page 16

research
03/31/2023

Extracting Thyroid Nodules Characteristics from Ultrasound Reports Using Transformer-based Natural Language Processing Methods

The ultrasound characteristics of thyroid nodules guide the evaluation o...
research
10/12/2021

Voice-assisted Image Labelling for Endoscopic Ultrasound Classification using Neural Networks

Ultrasound imaging is a commonly used technology for visualising patient...
research
06/09/2023

Automated Labeling of German Chest X-Ray Radiology Reports using Deep Learning

Radiologists are in short supply globally, and deep learning models offe...
research
07/08/2020

Labelling imaging datasets on the basis of neuroradiology reports: a validation study

Natural language processing (NLP) shows promise as a means to automate t...
research
04/03/2023

Efficient human-in-loop deep learning model training with iterative refinement and statistical result validation

Annotation and labeling of images are some of the biggest challenges in ...
research
06/15/2018

A Scalable Machine Learning Approach for Inferring Probabilistic US-LI-RADS Categorization

We propose a scalable computerized approach for large-scale inference of...
research
08/04/2019

Automated Corrosion Detection Using Crowd Sourced Training for Deep Learning

The automated detection of corrosion from images (i.e., photographs) or ...

Please sign up or login with your details

Forgot password? Click here to reset