3D G-CNNs for Pulmonary Nodule Detection

By Marysia Winkels, Taco Cohen. 2018

111 citations recorded by the source corpus.

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.

Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging, Model Architectures, Medical Imaging

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Citations in the corpus

General E(2)-Equivariant Steerable CNNs (2019, methodology): “Via a group pooling or projection operation max : R c → R , f ( x ) → max( f ( x )) the works [6, 7, 9, 32, 31] extract the maximum value of a regular or quotient ﬁeld.”
Gauge Equivariant Convolutional Networks and the Icosahedral CNN (2019, neutral): “For further study, we recommend (Sharpe, 1997; Shoshichi Kobayashi, 1963; Husem¨oller, 1994; Steenrod, 1951; Wendl, 2008; Marsh, 2016; Crane, 2014).”
A General Theory of Equivariant CNNs on Homogeneous Spaces (2018, background): “Some other examples include an RGB image (ρ(h) = I3), a field of wind directions on earth (ρ(h) a 2×2 rotation matrix), a diffusion tensor MRI image (ρ(h) a representation of SO(3) acting on 2-tensors), a regular G-CNN on Z(3) [14, 15] (ρ a regular representation of H).”
B-Spline CNNs on Lie Groups (2019, background): “It can be roughly divided into work on discrete G-CNNs (Cohen & Welling, 2016; Dieleman et al., 2016; Winkels & Cohen, 2018; Worrall & Brostow, 2018; Hoogeboom et al., 2018), regular continuous G-CNNs (Oyallon & Mallat, 2015; Bekkers et al., 2015a; 2018b; Weiler et al., 2018a; Zhou et al., 2017;…”
On Learning Sets of Symmetric Elements (2020, unknown): “…rotations (Dieleman et al., 2016; Cohen & Welling, 2016a;b; Worrall et al., 2017), spherical symmetries (Cohen et al., 2018; 2019b; Esteves et al., 2017), or 3D symmetries (Weiler et al., 2018; Winkels & Cohen, 2018; Worrall & Brostow, 2018; Kondor, 2018; Thomas et al., 2018; Weiler et al., 2018).”
Attentive Group Equivariant Convolutional Networks (2020, methodology): “Group convolutional networks have proven very successful in medical imaging applications (Bekkers et al., 2018; Winkels & Cohen, 2018; Lafarge et al., 2020).”
Geometric and Physical Quantities improve E(3) Equivariant Message Passing (2021, background): “Alternatively, the kernels are only transformed via a sub-group of transformations in E( 3 ) that leaves the grid on which the kernel is deﬁned intact, as in (Worrall & Brostow, 2018; Winkels & Cohen, 2018).”