Harshita Sharma

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BACKGROUND Computer-based analysis of digitalized histological images has been gaining increasing attention, due to their extensive use in research and routine practice. The article aims to contribute towards the description and retrieval of histological images by employing a structural method using graphs. Due to their expressive ability, graphs are(More)
Deep learning using convolutional neural networks is an actively emerging field in histological image analysis. This study explores deep learning methods for computer-aided classification in H&E stained histopathological whole slide images of gastric carcinoma. An introductory convolutional neural network architecture is proposed for two computerized(More)
The explosive growth of data produced by different devices and applications has contributed to the abundance of big data. To process such amounts of data efficiently, strategies such as De-duplication has been employed. Among the three different levels of de-duplication named as file level, block level and chunk level, De-duplication at chunk level also(More)
This paper describes a multi-resolution technique to combine diagnostically important visual information at different magnifications in H&E whole slide images (WSI) of gastric cancer. The primary goal is to improve the results of nuclei segmentation method for heterogeneous histopathological datasets with variations in stain intensity and malignancy levels.(More)
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