Introducing instance label correlation in multiple instance learning.
  Application to cancer detection on histopathological images
Pablo Morales-Álvarez Arne Schmidt José Miguel Hernández-Lobato Rafael Molina
In the last years, the weakly supervised paradigm of multiple instance
learning (MIL) has become very popular in many different areas. A paradigmatic
example is computational pathology, where the lack of patch-level labels for
whole-slide images prevents the application of supervised models. Probabilistic
MIL methods based on Gaussian Processes (GPs) have obtained promising results
due to their excellent uncertainty estimation capabilities. However, these are
general-purpose MIL methods that do not take into account one important fact:
in (histopathological) images, the labels of neighboring patches are expected
to be correlated. In this work, we extend a state-of-the-art GP-based MIL
method, which is called VGPMIL-PR, to exploit such correlation. To do so, we
develop a novel coupling term inspired by the statistical physics Ising model.
We use variational inference to estimate all the model parameters.
Interestingly, the VGPMIL-PR formulation is recovered when the weight that
regulates the strength of the Ising term vanishes. The performance of the
proposed method is assessed in two real-world problems of prostate cancer
detection. We show that our model achieves better results than other
state-of-the-art probabilistic MIL methods. We also provide different
visualizations and analysis to gain insights into the influence of the novel
Ising term. These insights are expected to facilitate the application of the
proposed model to other research areas.