  Although the U-Net architecture has been extensively used for segmentation of
medical images, we address two of its shortcomings in this work. Firstly, the
accuracy of vanilla U-Net degrades when the target regions for segmentation
exhibit significant variations in shape and size. Even though the U-Net already
possesses some capability to analyze features at various scales, we propose to
explicitly add multi-scale feature maps in each convolutional module of the
U-Net encoder to improve segmentation of histology images. Secondly, the
accuracy of a U-Net model also suffers when the annotations for supervised
learning are noisy or incomplete. This can happen due to the inherent
difficulty for a human expert to identify and delineate all instances of
specific pathology very precisely and accurately. We address this challenge by
introducing auxiliary confidence maps that emphasize less on the boundaries of
the given target regions. Further, we utilize the bootstrapping properties of
the deep network to address the missing annotation problem intelligently. In
our experiments on a private dataset of breast cancer lymph nodes, where the
primary task was to segment germinal centres and sinus histiocytosis, we
observed substantial improvement over a U-Net baseline based on the two
proposed augmentations.
