  Segmenting medical images accurately and reliably is important for disease
diagnosis and treatment. It is a challenging task because of the wide variety
of objects' sizes, shapes, and scanning modalities. Recently, many
convolutional neural networks (CNN) have been designed for segmentation tasks
and achieved great success. Few studies, however, have fully considered the
sizes of objects, and thus most demonstrate poor performance for small objects
segmentation. This can have a significant impact on the early detection of
diseases. This paper proposes a Context Axial Reserve Attention Network
(CaraNet) to improve the segmentation performance on small objects compared
with several recent state-of-the-art models. We test our CaraNet on brain tumor
(BraTS 2018) and polyp (Kvasir-SEG, CVC-ColonDB, CVC-ClinicDB, CVC-300, and
ETIS-LaribPolypDB) segmentation datasets. Our CaraNet achieves the top-rank
mean Dice segmentation accuracy, and results show a distinct advantage of
CaraNet in the segmentation of small medical objects.
