  We present our novel deep multi-task learning method for medical image
segmentation. Existing multi-task methods demand ground truth annotations for
both the primary and auxiliary tasks. Contrary to it, we propose to generate
the pseudo-labels of an auxiliary task in an unsupervised manner. To generate
the pseudo-labels, we leverage Histogram of Oriented Gradients (HOGs), one of
the most widely used and powerful hand-crafted features for detection. Together
with the ground truth semantic segmentation masks for the primary task and
pseudo-labels for the auxiliary task, we learn the parameters of the deep
network to minimise the loss of both the primary task and the auxiliary task
jointly. We employed our method on two powerful and widely used semantic
segmentation networks: UNet and U2Net to train in a multi-task setup. To
validate our hypothesis, we performed experiments on two different medical
image segmentation data sets. From the extensive quantitative and qualitative
results, we observe that our method consistently improves the performance
compared to the counter-part method. Moreover, our method is the winner of
FetReg Endovis Sub-challenge on Semantic Segmentation organised in conjunction
with MICCAI 2021.
