Leveraging Mid-Level Semantic Boundary Cues for Automated Lymph Node Detection
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The paper's method achieves 86% sensitivity at 3 false positives per patient on mediastinal LN detection, surpassing prior state-of-the-art sensitivities of 78% and 70%.
“We obtain 86% sensitivity on 3 false positives (FP) per patient, versus the prior state-of-art sensitivities of 78% [36] (stacked shallow learning) and 70% [22] (CNN), as prior state-of-the-art.”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [36]
Many of our CNN models achieve notably better FROC-AUC and TPR/3FP results than previous state-of-the-art models for mediastinal LN detection; GoogLeNet-RI-L obtains AUC=0.95 and 0.85 TPR/3FP.
“GoogLeNet-RI-L | 0.95 | 0.85 | 0.91 | 0.69”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [36]
The difference in performance lies in the fact that annotated LN segmentation masks are required to learn a mid-level semantic boundary detector, whereas CNN approaches only need LN locations for training.
“This difference lies in the fact that annotated lymph node segmentation masks are required to learn a mid-level semantic boundary detector [36], whereas CNN approaches only need LN locations for training [22].”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [36]
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- Authors
- Seff, A., Lu, L., Barbu, A., Roth, H., Shin, H.-C., & Summers, R. M.
- Affiliations
- National Institutes of Health (US)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2015
- Pages
- 53-61
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2229476080
- Cited by
- 36
- References
- 17
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 1 · 2022: 7 · 2021: 5 · 2020: 6 · 2019: 3 · 2018: 3 · 2017: 2 · 2016: 4
- FWCI
- 1.69
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Lecture notes in computer science (publishedVersion)
- Funders
- National Institutes of Health
- NIH Clinical Center
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- lymph node detection, histogram of oriented gradients, thoracoabdominal CT, computer-aided detection, mediastinum
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