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Project channel on Discord: #dicom-wsi
The Imaging Data Commons (IDC) portal is a cloud based repository of public cancer imaging data, and inclludes access to histopathological image data in the DICOM standard. In recent years increasingly more automated image analysis algorithms for pathology data emerged. However they are mainly developed for proprietary WSI vendor file formats and not for the standardized DICOM WSI file format as input. In this project a tumor classification algorithm shall be developed for DICOM WSI files as input file format. The aim of this project is to develop an end to end Google Colab notebook to define a data cohort from the IDC database. The selected cohort is retrieved in the DICOM WSI standard and an examplary deep learning based image analysis algorithm is applied on the selected DICOM studies. As a use case task we select a classification algorithm, which is a common downstream task in computational pathology. The algorithm generates for the selected DICOM WSI files a tumor heatmap and provides the analysis result as DICOM parametric map, which can be visualized together with WSI image in the interoperable web-based slide microscopy viewer and annotation tool (SLIM), which is fully integrated in the IDC database. A basis of this project provides the DICOM WSI support in the Kaapana platform, which also shall be improved in this project. The custom DICOM conversion pipeline for WSI files, which is integrated in the platform shall be improved and further applications with DICOM WSI data integrated in the platform.