A deep learning method for automatic evaluation of diagnostic information from multi-stained histopathological images

is developed based on a convolutional siamese network, in which the information quantification task is transformed into a similarity assessment between lesion and non-lesion patterns on histopathological images. The subtle changes underlying the microstructure of tissue biopsies can be captured through an optimization of training loss within a low-to-high-level feature space. A new information score is introduced to quantify the abnormality in tissue appearance and stain pattern. Experiments on 3 independent data cohorts including 5 types of color-stained images demonstrate that our method can achieve promising performance compared with state-of-the-art methods. Results show that the proposed information score can serve as an effective measure to evaluate the importance of multi-stained images, and ultimately facilitate automatic diagnosis for clinical multi-stained histopathology.