Whole-Slide Images and Patches of Clear Cell Renal Cell Carcinoma Tissue Sections Counterstained with Hoechst 33342, CD3, and CD8 Using Multiple Immunofluorescence

In recent years, there has been an increased effort to digitise whole-slide images of cancer tissue. This opened up a range new avenues for the application deep learning in oncology. One such avenue is virtual staining, where model tasked with reproducing appearance stained tissue sections, conditioned on different, often times less expensive, input stain. However, data train models supervised manner and output stains are aligned same sections scarce. this work, we introduce dataset ten clear cell renal carcinoma counterstained Hoechst 33342, CD3, CD8 using multiple immunofluorescence. We also provide set over 600,000 patches size 256 × pixels extracted from these together segmentation masks format amenable training models. It our hope that will be used further development methods digital pathology by serving as comparing benchmarking staining