End-to-end learning for detecting MYC translocations

Recent developments have improved whole-slide image classification to the point where the
entire slide can be analyzed using only weak labels, whilst retaining both local and global
context. In this paper, we use an end-to-end whole-slide image classification approach
using weak labels to classify MYC translocations in slides of diffuse large B-cell lymphoma.
Our model is able to achieve an AUC of 0.8012, which indicates the possibility of learning
relevant features for MYC translocations.