mlsanityData to 7 Oct 2026

3D G-CNNs for Pulmonary Nodule Detection

arXiv 1804.04656 ↗First posted 12 Apr 2018cs.LG stat.MLcited by 36

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Abstract (arXiv)

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.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Apr 2018.

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Citing papers per year: 2021: 9; 2022: 8; 2023: 5; 2024: 9; 2025: 3; 2026: 2.

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