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ROSNet: Robust one-stage network for CT lesion detection
- Source :
- Pattern Recognition Letters. 144:82-88
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Automatic lesion detection from computed tomography (CT) scans is an important task in medical diagnosis. However, three frequent properties of medical data make CT lesion detection a challenging task: (1) Scale variance: Large scale variation is across lesion instances. Especially, it is extremely difficult to detect small lesions; (2) Imbalanced data: The data distributions are highly imbalanced, where few classes account for the majority of data; (3) Prediction stability: Based on our observations, an input lesion image with slightly pixel shift or translation can lead to drastic output mispredictions and this is not allowed for medical applications. To address these challenges, this paper proposes a Robust One-Stage Network (ROSNet) for robust CT lesion detection. Specifically, a novel nested structure of neural networks is developed to generate a series of feature pyramids for detecting CT lesions in various scales, an effective data sensitive class-balanced loss as well as a shift-invariant downsampling strategy are also introduced to improve the detection performance. Experiments are conducted on a large-scale and diverse dataset, DeepLesion, showing that ROSNet outperforms the best performance in MICCAI 2019 by 3.95% (2-class detection task) and 25.41% (8-class detection task) in terms of mean average precision (mAP).
- Subjects :
- Pixel
medicine.diagnostic_test
Computer science
business.industry
Deep learning
Pattern recognition
Computed tomography
02 engineering and technology
01 natural sciences
Task (project management)
Lesion
Artificial Intelligence
Feature (computer vision)
0103 physical sciences
Signal Processing
0202 electrical engineering, electronic engineering, information engineering
medicine
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
Artificial intelligence
Medical diagnosis
medicine.symptom
010306 general physics
business
Software
Subjects
Details
- ISSN :
- 01678655
- Volume :
- 144
- Database :
- OpenAIRE
- Journal :
- Pattern Recognition Letters
- Accession number :
- edsair.doi...........005cd9935ebf226b4f2956d95725e7e9