Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge
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Abstract (arXiv)
We present a deep learning framework for computer-aided lung cancer diagnosis. Our multi-stage framework detects nodules in 3D lung CAT scans, determines if each nodule is malignant, and finally assigns a cancer probability based on these results. We discuss the challenges and advantages of our framework. In the Kaggle Data Science Bowl 2017, our framework ranked 41st out of 1972 teams.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 May 2017.
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Citing papers per year: 2021: 3; 2022: 4; 2023: 2; 2024: 0; 2025: 0; 2026: 2.
11 papers.
- 2022Generative Adversarial Networks for Image Super-Resolution: A Surveycited by 11
- 2021Learning Efficient, Explainable and Discriminative Representations for Pulmonary Nodules Classificationcited by 7
- 2021Segmentation of Lung Tumor from CT Images using Deep Supervisioncited by 2
- 2022Deep Learning Applications for Lung Cancer Diagnosis: A systematic reviewcited by 2
- 2022Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learningcited by 2
- 2022Efficient Lung Cancer Image Classification and Segmentation Algorithm Based on Improved Swin Transformercited by 1
- 2023TinyML Design Contest for Life-Threatening Ventricular Arrhythmia Detectioncited by 1
- 2021Detection, growth quantification and malignancy prediction of pulmonary nodules using deep convolutional networks in follow-up CT scanscited by 0
- 2023Swin-Tempo: Temporal-Aware Lung Nodule Detection in CT Scans as Video Sequences Using Swin Transformer-Enhanced UNetcited by 0
- 2026Distilling Photon-Counting CT into Routine Chest CT through Clinically Validated Degradation Modelingcited by 0
- 2026The ecosystem of machine learning competitions: Platforms, participants, and their impact on AI developmentcited by 0
- 2026The ecosystem of machine learning competitions: Platforms, participants, and their impact on AI development9 Apr 2026
- 2026Distilling Photon-Counting CT into Routine Chest CT through Clinically Validated Degradation Modeling8 Apr 2026
- 2023Swin-Tempo: Temporal-Aware Lung Nodule Detection in CT Scans as Video Sequences Using Swin Transformer-Enhanced UNet5 Oct 2023
- 2023TinyML Design Contest for Life-Threatening Ventricular Arrhythmia Detection9 May 2023
- 2022Efficient Lung Cancer Image Classification and Segmentation Algorithm Based on Improved Swin Transformer4 Jul 2022
- 2022Generative Adversarial Networks for Image Super-Resolution: A Survey28 Apr 2022
- 2022Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learning30 Mar 2022
- 2022Deep Learning Applications for Lung Cancer Diagnosis: A systematic review1 Jan 2022
- 2021Segmentation of Lung Tumor from CT Images using Deep Supervision17 Nov 2021
- 2021Detection, growth quantification and malignancy prediction of pulmonary nodules using deep convolutional networks in follow-up CT scans26 Mar 2021
- 2021Learning Efficient, Explainable and Discriminative Representations for Pulmonary Nodules Classification19 Jan 2021