  Pancreatic ductal adenocarcinoma (PDAC) is the third most common cause of
cancer death in the United States. Predicting tumors like PDACs (including both
classification and segmentation) from medical images by deep learning is
becoming a growing trend, but usually a large number of annotated data are
required for training, which is very labor-intensive and time-consuming. In
this paper, we consider a partially supervised setting, where cheap image-level
annotations are provided for all the training data, and the costly per-voxel
annotations are only available for a subset of them. We propose an Inductive
Attention Guidance Network (IAG-Net) to jointly learn a global image-level
classifier for normal/PDAC classification and a local voxel-level classifier
for semi-supervised PDAC segmentation. We instantiate both the global and the
local classifiers by multiple instance learning (MIL), where the attention
guidance, indicating roughly where the PDAC regions are, is the key to bridging
them: For global MIL based normal/PDAC classification, attention serves as a
weight for each instance (voxel) during MIL pooling, which eliminates the
distraction from the background; For local MIL based semi-supervised PDAC
segmentation, the attention guidance is inductive, which not only provides
bag-level pseudo-labels to training data without per-voxel annotations for MIL
training, but also acts as a proxy of an instance-level classifier.
Experimental results show that our IAG-Net boosts PDAC segmentation accuracy by
more than 5% compared with the state-of-the-arts.
