  Researchers working on computational analysis of Whole Slide Images (WSIs) in
histopathology have primarily resorted to patch-based modelling due to large
resolution of each WSI. The large resolution makes WSIs infeasible to be fed
directly into the machine learning models due to computational constraints.
However, due to patch-based analysis, most of the current methods fail to
exploit the underlying spatial relationship among the patches. In our work, we
have tried to integrate this relationship along with feature-based correlation
among the extracted patches from the particular tumorous region. For the given
task of classification, we have used BiLSTMs to model both forward and backward
contextual relationship. RNN based models eliminate the limitation of sequence
size by allowing the modelling of variable size images within a deep learning
model. We have also incorporated the effect of spatial continuity by exploring
different scanning techniques used to sample patches. To establish the
efficiency of our approach, we trained and tested our model on two datasets,
microscopy images and WSI tumour regions. After comparing with contemporary
literature we achieved the better performance with accuracy of 90% for
microscopy image dataset. For WSI tumour region dataset, we compared the
classification results with deep learning networks such as ResNet, DenseNet,
and InceptionV3 using maximum voting technique. We achieved the highest
performance accuracy of 84%. We found out that BiLSTMs with CNN features have
performed much better in modelling patches into an end-to-end Image
classification network. Additionally, the variable dimensions of WSI tumour
regions were used for classification without the need for resizing. This
suggests that our method is independent of tumour image size and can process
large dimensional images without losing the resolution details.
