Locally-run Interpretable Breast Cancer diagnosis from Histology Images

In recent years, Artificial Intelligence (AI) has made tremendous advances in identifying diseases from radiology images. Convolutional Neural Networks (CNNs), a class of deep learning algorithm trained on large volumes of labelled radiological images, have led these advances. Various results has shown that CNNs improves the speed, accuracy and consistency of diagnosis.

However, the adoption of deep learning diagnostic system by healthcare practitioners is prevented by two major challenges: 1) interpreting the prediction outputs from a deep learning network is not trivial, and 2) Privacy of patient data is not guaranteed when using online services that provides deep learning models. These challenges are why healthcare practitioners remain wary of using AI-driven diagnostic tools.

A medical practitioner cannot fully trust the CNN network except it can explain its reason for its decision, semantically or visually. Earlier methods in machine learning are transparent in how they compute the predictions but deep learning models are not so. Deep learning models automate the hand crafted feature engineering and hence no knowledge of how the predictions are computed. Diagnosing with CNN involves studying image regions that contribute most to prediction outputs at the pixel level. In interpretability, we expect the CNN to explain its decision at the object-part level. Given an interpret-able CNN, previous work reveals the distribution of object parts that are memorized by the CNN for object classification.

In addressing the second motivation behind this work, a CNN model is being deployed through a server client architecture which requires the data to be sent online to the model for prediction. Deep learning models are large in memory and computation. Hence, they need large computing power like GPUs that requires an existing remote servers. To get predictions, doctors have to upload the patient radiological scan through the internet exposing it to the risk of data privacy. What we have done in this work instead, is to use solutions that make such models run locally thereby solving the issue of privacy. This technique also solves the challenge faced in developing countries where access to internet could be expensive.