3D G-CNNs for Pulmonary Nodule Detection

04/12/2018
by   Marysia Winkels, et al.
0

Convolutional Neural Networks (CNNs) require a large amount of annotated data to learn from, which is often difficult to obtain in the medical domain. In this paper we show that the sample complexity of CNNs can be significantly improved by using 3D roto-translation group convolutions (G-Convs) instead of the more conventional translational convolutions. These 3D G-CNNs were applied to the problem of false positive reduction for pulmonary nodule detection, and proved to be substantially more effective in terms of performance, sensitivity to malignant nodules, and speed of convergence compared to a strong and comparable baseline architecture with regular convolutions, data augmentation and a similar number of parameters. For every dataset size tested, the G-CNN achieved a FROC score close to the CNN trained on ten times more data.

READ FULL TEXT
research
02/24/2016

Group Equivariant Convolutional Networks

We introduce Group equivariant Convolutional Neural Networks (G-CNNs), a...
research
02/27/2018

Matching Convolutional Neural Networks without Priors about Data

We propose an extension of Convolutional Neural Networks (CNNs) to graph...
research
03/03/2023

Linear CNNs Discover the Statistical Structure of the Dataset Using Only the Most Dominant Frequencies

Our theoretical understanding of the inner workings of general convoluti...
research
03/15/2023

Trigger-Level Event Reconstruction for Neutrino Telescopes Using Sparse Submanifold Convolutional Neural Networks

Convolutional neural networks (CNNs) have seen extensive applications in...
research
12/11/2020

Cyclic orthogonal convolutions for long-range integration of features

In Convolutional Neural Networks (CNNs) information flows across a small...
research
03/09/2020

Single-view 2D CNNs with Fully Automatic Non-nodule Categorization for False Positive Reduction in Pulmonary Nodule Detection

Background and Objective: In pulmonary nodule detection, the first stage...
research
12/08/2016

Filter sharing: Efficient learning of parameters for volumetric convolutions

Typical convolutional neural networks (CNNs) have several millions of pa...

Please sign up or login with your details

Forgot password? Click here to reset