Figure 1.

Overview of DeepLIIF pipeline and sample input IHCs (different brown/DAB markers – BCL2, BCL6, CD10, CD3/CD8, Ki67) with corresponding DeepLIIF-generated hematoxylin/mpIF modalities and classified (positive (red) and negative (blue) cell) segmentation masks. (a) Overview of DeepLIIF. Given an IHC input, our multitask deep learning framework simultaneously infers corresponding Hematoxylin channel, mpIF DAPI, mpIF protein expression (Ki67, CD3, CD8, etc.), and the positive/negative protein cell segmentation, baking explainability and interpretability into the model itself rather than relying on coarse activation/attention maps. In the segmentation mask, the red cells denote cells with positive protein expression (brown/DAB cells in the input IHC), whereas blue cells represent negative cells (blue cells in the input IHC). (b) Example DeepLIIF-generated hematoxylin/mpIF modalities and segmentation masks for different IHC markers. DeepLIIF, trained on clean IHC Ki67 nuclear marker images, can generalize to noisier as well as other IHC nuclear/cytoplasmic marker images.