ArticleInternational journal of computer assisted radiology and surgery2020
Unravelling the effect of data augmentation transformations in polyp segmentation.
Article in International journal of computer assisted radiology and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Efficiency of oral keratinized gingiva detection and measurement based on convolutional neural network.Journal of periodontology · 2025Article
- GAN Inversion for Data Augmentation to Improve Colonoscopy Lesion Classification.IEEE journal of biomedical and health informatics · 2025Article
- Detecting early gastrointestinal polyps in histology and endoscopy images using deep learning.Frontiers in artificial intelligence · 2025Article
- Diagnosis of ophthalmologic diseases in canines based on images using neural networks for image segmentation.Heliyon · 2024Article
- ESFPNet: Efficient Stage-Wise Feature Pyramid on Mix Transformer for Deep Learning-Based Cancer Analysis in Endoscopic Video.Journal of imaging · 2024Article
- Dual ensemble system for polyp segmentation with submodels adaptive selection ensemble.Scientific reports · 2024Article
- An approach to the diagnosis of lumbar disc herniation using deep learning models.Frontiers in bioengineering and biotechnology · 2023Article
- Investigation of biases in convolutional neural networks for semantic segmentation using performance sensitivity analysis.Zeitschrift fur medizinische Physik · 2022Article
- Novel Pixelwise Co-Registered Hematoxylin-Eosin and Multiphoton Microscopy Image Dataset for Human Colon Lesion Diagnosis.Journal of pathology informatics · 2022Article
- Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation.Frontiers in neuroimaging · 2022Article
- A Deep Learning Image Data Augmentation Method for Single Tumor Segmentation.Frontiers in oncology · 2022Article
- The Clinician's Guide to the Machine Learning Galaxy.Frontiers in physiology · 2021Article
- Lumbar Disc Herniation Automatic Detection in Magnetic Resonance Imaging Based on Deep Learning.Frontiers in bioengineering and biotechnology · 2021Article
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4 authors.
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Abstract
purposeData augmentation is a common technique to overcome the lack of large annotated databases, a usual situation when applying deep learning to medical imaging problems. Nevertheless, there is no consensus on which transformations to apply for a particular field. This work aims at identifying the effect of different transformations on polyp segmentation using deep learning.
methodsA set of transformations and ranges have been selected, considering image-based (width and height shift, rotation, shear, zooming, horizontal and vertical flip and elastic deformation), pixel-based (changes in brightness and contrast) and application-based (specular lights and blurry frames) transformations. A model has been trained under the same conditions without data augmentation transformations (baseline) and for each of the transformation and ranges, using CVC-EndoSceneStill and Kvasir-SEG, independently. Statistical analysis is performed to compare the baseline performance against results of each range of each transformation on the same test set for each dataset.
resultsThis basic method identifies the most adequate transformations for each dataset. For CVC-EndoSceneStill, changes in brightness and contrast significantly improve the model performance. On the contrary, Kvasir-SEG benefits to a greater extent from the image-based transformations, especially rotation and shear. Augmentation with synthetic specular lights also improves the performance.
conclusionDespite being infrequently used, pixel-based transformations show a great potential to improve polyp segmentation in CVC-EndoSceneStill. On the other hand, image-based transformations are more suitable for Kvasir-SEG. Problem-based transformations behave similarly in both datasets. Polyp area, brightness and contrast of the dataset have an influence on these differences.
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