  To alleviate the burden of gathering detailed expert annotations when
training deep neural networks, we propose a weakly supervised learning approach
to recognize metastases in microscopic images of breast lymph nodes. We
describe an alternative training loss which clusters weakly labeled bags in
latent space to inform relevance of patch-instances during training of a
convolutional neural network. We evaluate our method on the Camelyon dataset
which contains high-resolution digital slides of breast lymph nodes, where
labels are provided at the image-level and only subsets of patches are made
available during training.
