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: this https URL

讨论使用弱监督深度学习算法，以减少复杂射电星系的像素级标签成本。使用澳大利亚平方公里阵列（ASKAP）望远镜的数据，展示了该算法在预测像素级信息方面的高准确性，包括射电辐射的掩蔽遥远星系中的所有成分和红外主机位置的掩模。