  Digital pathology and microscopy image analysis are widely employed in the
segmentation of digitally scanned IHC slides, primarily to identify cancer and
pinpoint regions of interest (ROI) indicative of tumor presence. However,
current ROI segmentation models are either stain-specific or suffer from the
issues of stain and scanner variance due to different staining protocols or
modalities across multiple labs. Also, tissues like Ductal Carcinoma in Situ
(DCIS), acini, etc. are often classified as Tumors due to their structural
similarities and color compositions. In this paper, we proposed a novel
convolutional neural network (CNN) based Multi-class Tissue Segmentation model
for histopathology whole-slide Breast slides which classify tumors and segments
other tissue regions such as Ducts, acini, DCIS, Squamous epithelium, Blood
Vessels, Necrosis, etc. as a separate class. Our unique pixel-aligned
non-linear merge across spatial resolutions empowers models with both local and
global fields of view for accurate detection of various classes. Our proposed
model is also able to separate bad regions such as folds, artifacts, blurry
regions, bubbles, etc. from tissue regions using multi-level context from
different resolutions of WSI. Multi-phase iterative training with context-aware
augmentation and increasing noise was used to efficiently train a multi-stain
generic model with partial and noisy annotations from 513 slides. Our training
pipeline used 12 million patches generated using context-aware augmentations
which made our model stain and scanner invariant across data sources. To
extrapolate stain and scanner invariance, our model was evaluated on 23000
patches which were for a completely new stain (Hematoxylin and Eosin) from a
completely new scanner (Motic) from a different lab. The mean IOU was 0.72
which is on par with model performance on other data sources and scanners.
