Recently, as the usage of electronic devices increase, modern people suffer from eye diseases. We analyzed goblet cells of wide-field fluorescence microscopy with a deep learning. In this study, we propose to real-time analysis using knowledge distillation using proposed loss function and optimized network. In the proposed method, residual based UNet was used as the teacher network to distill knowledge into lightweight E-Net. We train the student network using pixelwise loss and . The proposed method showed 4% improvements in dice-score compared to the lightweight E-Net, and the processing time was decreased to 68% compared to the case where only the teacher network was performed.
Dielectrophoresis is a technology that uses the electrical properties of cells to control the movement of cells in a non-contact manner. It is important to observe cell movement in order to analyze cell characteristics using DEP technology. We developed an algorithm that can track the movement of hundreds of unlabeled cells by DEP force. The proposed algorithm consists of a cell detection step using a deep learning detection model and a cell tracking step based on a multiscale region of interest. Cell detection and tracking accuracy using Recall, precision, f-measure, and MOTA on a timelapse microscope image dataset has an accuracy of about 97% or more. In conclusion, by developing an automated tool that can perform imaging-based DEP cell analysis, cell tracking algorithms that can track hundreds of cells simultaneously can reduce cell analysis time and labor.
Arrhythmia is the heartbeat losing its regularity or deviating from its average number. Among the types of arrhythmia is atrial fibrillation (AF) and atrial flutter (AFL), which are considered risk factors for development due to high morbidity and mortality. The early detection of AF/AFL is essential because their effects on the heart or complications appear after a considerable time. Electrocardiography (ECG) is a widely used screening method in primary care because of its low cost and convenience. ECG records the heart's electrical activity for a period of time via electrodes attached to the body. Owing to the development of computing power and interest in big data, attempts at deep learning (DL) have increased. The transformer was proposed by Google in 2017 and has achieved state-of-the-art performance in natural language processing. Various transformer-based models have been applied to various tasks beyond natural language processing and have shown promising prospects. However, there have been few cases of vision transformer (ViT) applications in ECG domain. It was difficult to determine whether ViT had sufficient influence in ECG domain. This study determined whether our extensive ECG dataset could make an AF/AFL diagnosis. We also confirmed whether the recently proposed ViT has AF/AFL diagnostic power.
Traditional methods of wound diagnosis have been diagnosed and prescribed by the naked eye of an expert. If the wound segmentation algorithm is applied to the wound diagnosis, the area of wound can be quantitated and used as an auxiliary means of treatment. Even with dramatic development of Deep learning technology in recent years, However, a lack of datasets generally occurs overfitting problem of deep learning model, which leads to poor performance for external datasets. Therefore, we trained the wound segmentation model by adding a new wound dataset in addition to the existing Open dataset, the Diabetic Foot Ulcer Challenge Dataset. Machine learning based methods are used when producing new dataset, ground truth images. Thus, in addition to the manual methods, Gradient Vector Flow machine learning techniques is used for ground-truth image production to reduce the time consumed in vain. The wound segmentation model used in this study is a U-net with residual block combined with cross entropy loss and Dice loss. As a result of the experiment, the wound segmentation accuracy was about 90% for Dice coefficient
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