Classifies whole-slide pathology images into adenocarcinoma, squamous cell carcinoma or normal lung tissue. DeepPATH has been trained on a deep learning convolutional neural network (CNN) model on histopathology images obtained from The Cancer Genome Atlas (TCGA) to arrange and organize pathology images. This software performs multi-task classification of genes to study the prediction of gene mutations from histopathology images.
Meets the growing need for a user-friendly, extensible, open-source solution for digital pathology and whole slide image analysis. QuPath provides researchers with powerful batch-processing and scripting functionality, and an extensible platform with which to develop and share new algorithms to analyze complex tissue images. It aims to help improve the speed, objectivity and reproducibility of digital pathology analysis and biomarker interpretation.
Detects patch-based carcinoma in confocal laser endomicroscopy (CLE) images. This patch probability fusion method that can provide additional real-time information about the suspicious lesion, supportive to the clinical examination. The software can serve as an additional tool supporting the biopsy and the following histopathological examination. It was applied on CLE images of oral squamous cell carcinoma (OSCC).
It is the ideal "glue" for easily integrating dissimilar fluorescent microscope hardware and peripherals into a single custom workstation, while providing all the tools needed to perform meaningful analysis of acquired images. The software offers many user-friendly application modules for biology-specific analysis such as cell signaling, cell counting, and protein expression.
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