Jun Hashimoto

Pattana Chintarungruangchai, Ing-Guey Jiang, Jun Hashimoto et al.

The method of direct imaging has detected many exoplanets and made important contribution to the field of planet formation. The standard method employs angular differential imaging (ADI) technique, and more ADI image frames could lead to the results with larger signal-to-noise-ratio (SNR). However, it would need precious observational time from large telescopes, which are always over-subscribed. We thus explore the possibility to generate a converter which can increase the SNR derived from a smaller number of ADI frames. The machine learning technique with two-dimension convolutional neural network (2D-CNN) is tested here. Several 2D-CNN models are trained and their performances of denoising are presented and compared. It is found that our proposed Modified five-layer Wide Inference Network with the Residual learning technique and Batch normalization (MWIN5-RB) can give the best result. We conclude that this MWIN5-RB can be employed as a converter for future observational data.

Hideaki Okamoto, Quan Huu Cap, Takakiyo Nomura et al.

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 taken by radiographers, thus allowing a much larger number of patients to undergo imaging. However, the diagnosis of X-ray images relies heavily on the expertise and experience of physicians, and few machine learning methods have been developed to assist in this process. We propose a novel and practical gastric cancer diagnostic support system for gastric X-ray images that will enable 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 and provides 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 achieved a sensitivity (SE) for gastric cancer of 90.2\%, higher than that of an expert (85.5%). Under these conditions, two out of five candidate boxes identified by the system were cancerous (precision = 42.5%), with an image processing speed of 0.51 seconds per image. The system also outperformed methods using the same object detection model and state-of-the-art data augmentation by showing a 5.9-point improvement in the F1 score. In summary, this system efficiently identifies areas for radiologists to examine within a practical time frame, thus significantly reducing their workload.