Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

DeepRadiologyNet: Radiologist Level Pathology Detection in CT Head Images

Domain:

healthcare

Record type:

papermodel
Creator:
MerLufNguSoa
Host:avatar
We describe a system to automatically filter clinically significant findings from computerized tomography (CT) head scans, operating at performance levels exceeding that of practicing radiologists. Our system, named DeepRadiologyNet, builds on top of deep convolutional neural networks (CNNs) trained using approximately 3.5 million CT head images gathered from over 24,000 studies taken from January 1, 2015 to August 31, 2015 and January 1, 2016 to April 30 2016 in over 80 clinical sites. For our initial system, we identified 30 phenomenological traits to be recognized in the CT scans. To test the system, we designed a clinical trial using over 4.8 million CT head images (29,925 studies), completely disjoint from the training and validation set, interpreted by 35 US Board Certified radiologists with specialized CT head experience. We measured clinically significant error rates to ascertain whether the performance of DeepRadiologyNet was comparable to or better than that of US Board Certified radiologists. DeepRadiologyNet achieved a clinically significant miss rate of 0.0367% on automatically selected high-confidence studies. Thus, DeepRadiologyNet enables significant reduction in the workload of human radiologists by automatically filtering studies and reporting on the high-confidence ones at an operating point well below the literal error rate for US Board Certified radiologists, estimated at 0.82%. 22 pages with references, 6 figures, 2 tables

Visit

arxiv.org

Tasks

computer visionimage classification

Tags

Computer Vision and Pattern Recognition

Similar

Lung Infection Detection via CT Images and Transfer Learning Techniques in Deep LearningWeakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT ImagesAutomated Lung Cancer Diagnosis Applying Butterworth Filtering, Bi-Level Feature Extraction, and Sparce Convolutional Neural Network to Luna 16 CT ImagesIncremental Learning Based Anomaly Detection For Computed Tomography (CT)CT-Malaria Detection via Adaptive-Weighted Deep Learning ModelsLymphatic Filariasis detection in microscopic images

Lung Infection Detection via CT Images and Transfer Learning Techniques in Deep Learning

Healthcare systems are battling the global coronavirus epidemic with limited resources, requiring ea

Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Images

An outbreak of a novel coronavirus disease (i.e., COVID-19) has been recorded in Wuhan, China since

Automated Lung Cancer Diagnosis Applying Butterworth Filtering, Bi-Level Feature Extraction, and Sparce Convolutional Neural Network to Luna 16 CT Images

Accurate prognosis and diagnosis are crucial for selecting and planning lung cancer treatments. As a

Incremental Learning Based Anomaly Detection For Computed Tomography (CT)

Incremental Learning Based Anomaly Detection For Computed Tomography (CT)

Poster presented at the Deep Learning Indaba 2022 by Oluwabukola Adegboro

CT-Malaria Detection via Adaptive-Weighted Deep Learning Models

Context: In numerous low- and middle-income nations, malaria remains a significant issue due to the

Lymphatic Filariasis detection in microscopic images

In Africa, the propagation of parasites like the lymphatic filariasis is complicating seriously the