Nodule detection and generation on chest X-rays: NODE21 Challenge
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929198122139648 |
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| author | Sogancioglu, Ecem van Ginneken, Bram Behrendt, Finn Bengs, Marcel Schlaefer, Alexander Radu, Miron Xu, Di Sheng, Ke Scalzo, Fabien Marcus, Eric Papa, Samuele Teuwen, Jonas Scholten, Ernst Th. Schalekamp, Steven Hendrix, Nils Jacobs, Colin Hendrix, Ward Sánchez, Clara I Murphy, Keelin |
| author_facet | Sogancioglu, Ecem van Ginneken, Bram Behrendt, Finn Bengs, Marcel Schlaefer, Alexander Radu, Miron Xu, Di Sheng, Ke Scalzo, Fabien Marcus, Eric Papa, Samuele Teuwen, Jonas Scholten, Ernst Th. Schalekamp, Steven Hendrix, Nils Jacobs, Colin Hendrix, Ward Sánchez, Clara I Murphy, Keelin |
| contents | Pulmonary nodules may be an early manifestation of lung cancer, the leading cause of cancer-related deaths among both men and women. Numerous studies have established that deep learning methods can yield high-performance levels in the detection of lung nodules in chest X-rays. However, the lack of gold-standard public datasets slows down the progression of the research and prevents benchmarking of methods for this task. To address this, we organized a public research challenge, NODE21, aimed at the detection and generation of lung nodules in chest X-rays. While the detection track assesses state-of-the-art nodule detection systems, the generation track determines the utility of nodule generation algorithms to augment training data and hence improve the performance of the detection systems. This paper summarizes the results of the NODE21 challenge and performs extensive additional experiments to examine the impact of the synthetically generated nodule training images on the detection algorithm performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_02192 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Nodule detection and generation on chest X-rays: NODE21 Challenge Sogancioglu, Ecem van Ginneken, Bram Behrendt, Finn Bengs, Marcel Schlaefer, Alexander Radu, Miron Xu, Di Sheng, Ke Scalzo, Fabien Marcus, Eric Papa, Samuele Teuwen, Jonas Scholten, Ernst Th. Schalekamp, Steven Hendrix, Nils Jacobs, Colin Hendrix, Ward Sánchez, Clara I Murphy, Keelin Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Pulmonary nodules may be an early manifestation of lung cancer, the leading cause of cancer-related deaths among both men and women. Numerous studies have established that deep learning methods can yield high-performance levels in the detection of lung nodules in chest X-rays. However, the lack of gold-standard public datasets slows down the progression of the research and prevents benchmarking of methods for this task. To address this, we organized a public research challenge, NODE21, aimed at the detection and generation of lung nodules in chest X-rays. While the detection track assesses state-of-the-art nodule detection systems, the generation track determines the utility of nodule generation algorithms to augment training data and hence improve the performance of the detection systems. This paper summarizes the results of the NODE21 challenge and performs extensive additional experiments to examine the impact of the synthetically generated nodule training images on the detection algorithm performance. |
| title | Nodule detection and generation on chest X-rays: NODE21 Challenge |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2401.02192 |