Stochastic Gastric Image Augmentation for Cancer Detection from X-ray Images
| Autor: | Jun Hashimoto, Quan Huu Cap, Hitoshi Iyatomi, Hideaki Okamoto, Takakiyo Nomura |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
medicine.medical_specialty
medicine.diagnostic_test Computer science Cancer Gastric fold Cancer detection medicine.disease Convolutional neural network 030218 nuclear medicine & medical imaging Endoscopy 03 medical and health sciences 0302 clinical medicine Computer-aided diagnosis 030220 oncology & carcinogenesis medicine X ray image Radiology |
| Zdroj: | IEEE BigData |
| DOI: | 10.1109/bigdata47090.2019.9006079 |
| Popis: | X-ray examinations are a common choice in mass screenings for gastric cancer. Compared to endoscopy and other common modalities, X-ray examinations have the significant advantage that they can be performed not only by radiologists but also by radiology technicians. However, the diagnosis of gastric X-ray images is very difficult and it has been reported that the diagnostic accuracy of these images is only 85.5%. In this study, we propose a practical diagnosis support system for gastric X-ray images. An important component of our system is the proposed on-line data augmentation strategy named stochastic gastric image augmentation (sGAIA), which stochastically generates various enhanced images of gastric folds in X-ray images. The proposed sGAIA improves the detection performance of the malignant region by 6.9% in F1-score and our system demonstrates promising screening performance for gastric cancer (recall of 92.3% with a precision of 32.4%) from X-ray images in a clinical setting based on Faster R-CNN with ResNetl01 networks. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|