Practical X-ray Gastric Cancer Screening Using Refined Stochastic Data Augmentation and Hard Boundary Box Training

• URL: http://arxiv.org/abs/2108.08158v4
• Date: Mon, 26 Aug 2024 06:30:52 GMT
• Title: Practical X-ray Gastric Cancer Screening Using Refined Stochastic Data Augmentation and Hard Boundary Box Training
• Authors: Hideaki Okamoto, Quan Huu Cap, Takakiyo Nomura, Kazuhito Nabeshima, Jun Hashimoto, Hitoshi Iyatomi,
• Abstract summary: The proposed system achieves a sensitivity (SE) for gastric cancer of 90.2%, higher than that of an expert (85.5%) Two out of five detected candidate boxes are cancerous, maintaining high precision while processing images at a speed of 0.51 seconds per image.
• Score: 2.254041925375415
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Endoscopy is widely used to diagnose gastric cancer and has a high diagnostic performance, but it must be performed by a physician, which limits the number of people who can be diagnosed. In contrast, gastric X-rays can be performed by technicians and screen a much larger number of patients, but accurate diagnosis requires experience. We propose an unprecedented and practical gastric cancer diagnosis support system for gastric X-ray images, enabling more people to be screened. The system is based on a general deep learning-based object detection model and incorporates two novel techniques: refined probabilistic stomach image augmentation (R-sGAIA) and hard boundary box training (HBBT). R-sGAIA enhances the probabilistic gastric fold region, providing more learning patterns for cancer detection models. HBBT is an efficient training method that improves model performance by allowing the use of unannotated negative (i.e., healthy control) samples, which are typically unusable in conventional detection models. The proposed system achieves a sensitivity (SE) for gastric cancer of 90.2%, higher than that of an expert (85.5%). Additionally, two out of five detected candidate boxes are cancerous, maintaining high precision while processing images at a speed of 0.51 seconds per image. The system also outperforms methods using the same object detection model and state-of-the-art data augmentation, showing a 5.9-point improvement in the F1 score. In summary, this system efficiently identifies areas for radiologists to examine within a practical timeframe, significantly reducing their workload.

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