Scene text localization is a very crucial step in the issue of scene text recognition. The major challenges—such as how there are various sizes, shapes, unpredictable orientations, a wide range of colors and styles, occlusion, and local and global illumination variations—make the problem different from generic object detection. Unlike existing scene text localization methods, here we present a segmentation-based text detector which can detect an arbitrary shaped scene text by using polygon offsetting, combined with the border augmentation. This technique better distinguishes contiguous and arbitrary shaped text instances from nearby non-text regions. The quantitative experimental results on public benchmarks, ICDAR2015, ICDAR2017-MLT, ICDAR2019-MLT, and Total-Text datasets demonstrate the performance and robustness of our proposed method, compared to previous approaches which have been proposed.

Glucose-6-phosphate dehydrogenase (G6PD) deficiency is the most common enzymopathy in humans. More than 400 million people worldwide are affected by this genetic condition. Testing for G6PD deficiency before drug administration is essential for patient safety. Rapidly ascertaining the G6PD status of a person is desirable for proper treatment. The device described in this study, the G6PD diaxBOX, was developed to quantify G6PD deficiency using paper-based analytical devices (PADs) and a colorimetric assay. The G6PD diaxBOX is a straightforward, affordable, portable, and instrument-free analytical system. The major components of the G6PD diaxBox are a banknote-checking UV fluorescent lamp and camera that are easy to access and analysis software. When NADPH is generated, it absorbs at UV 340 nm and emits colored light that is detected with the camera. The determined Pearson's coefficient shows that the color intensity measured from the G6PD diaxBOX correlated with G6PD activity level. Also, a Bland-Altman analysis indicated that more than 95% of the measurement error was in the upper and lower boundaries (+/- 2 SD) and the error from the severe and moderate deficiency group was less than +/- 1 SD. Therefore, the error from G6PD diaxBOX was within the limit boundary and the overall accuracy was more than 80%. The G6PD diaxBOX facilitates the effective and efficient quantification of G6PD deficiency and as such represents a clinically well-suited, rapid point-of-care test.

Liver cancer remains a significant global health concern, and early-stage differential diagnosis and treatment can dramatically improve patient survival rates. However, current diagnostic methods are challenging and subjective, highlighting the need for accurate diagnostic support tools. This paper introduces a deep learning-based diagnostic framework for detecting liver tumor malignancy using DCE-MRI, particularly in scenarios with limited well-annotated data. To address the overfitting issue, we propose a Siamese convolutional neural network incorporating metric learning concepts, emphasizing discriminative representation learning. The core principle of our framework is to reorganize the embedding space such that similar sample pairs are closer together while dissimilar ones are further apart. Once the embedding space is learned, unseen samples can be classified based on a distance metric to a few seen samples within the embedding space. Comprehensive experiments on a professionally annotated, self-established dataset demonstrate that our framework outperforms fully supervised approaches in data scarcity situations, achieving a sensitivity of 96%, precision of 82%, and an F1-score of 88%.