Deep Learning for Morphological Identification of Extended Radio
  Galaxies using Weak Labels
Nikhel Gupta Zeeshan Hayder Ray P. Norris Minh Huynh Lars Petersson X. Rosalind Wang Heinz Andernach Bärbel S. Koribalski Miranda Yew Evan J. Crawford
The present work discusses the use of a weakly-supervised deep learning
algorithm that reduces the cost of labelling pixel-level masks for complex
radio galaxies with multiple components. The algorithm is trained on weak
class-level labels of radio galaxies to get class activation maps (CAMs). The
CAMs are further refined using an inter-pixel relations network (IRNet) to get
instance segmentation masks over radio galaxies and the positions of their
infrared hosts. We use data from the Australian Square Kilometre Array
Pathfinder (ASKAP) telescope, specifically the Evolutionary Map of the Universe
(EMU) Pilot Survey, which covered a sky area of 270 square degrees with an RMS
sensitivity of 25-35 $\mu$Jy/beam. We demonstrate that weakly-supervised deep
learning algorithms can achieve high accuracy in predicting pixel-level
information, including masks for the extended radio emission encapsulating all
galaxy components and the positions of the infrared host galaxies. We evaluate
the performance of our method using mean Average Precision (mAP) across
multiple classes at a standard intersection over union (IoU) threshold of 0.5.
We show that the model achieves a mAP$_{50}$ of 67.5\% and 76.8\% for radio
masks and infrared host positions, respectively. The network architecture can
be found at the following link: https://github.com/Nikhel1/Gal-CAM