ASF-YOLO: A Novel YOLO Model with Attentional Scale Sequence Fusion for
  Cell Instance Segmentation
Ming Kang Chee-Ming Ting Fung Fung Ting Raphaël C. -W. Phan
We propose a novel Attentional Scale Sequence Fusion based You Only Look Once
(YOLO) framework (ASF-YOLO) which combines spatial and scale features for
accurate and fast cell instance segmentation. Built on the YOLO segmentation
framework, we employ the Scale Sequence Feature Fusion (SSFF) module to enhance
the multi-scale information extraction capability of the network, and the
Triple Feature Encoder (TPE) module to fuse feature maps of different scales to
increase detailed information. We further introduce a Channel and Position
Attention Mechanism (CPAM) to integrate both the SSFF and TPE modules, which
focus on informative channels and spatial position-related small objects for
improved detection and segmentation performance. Experimental validations on
two cell datasets show remarkable segmentation accuracy and speed of the
proposed ASF-YOLO model. It achieves a box mAP of 0.91, mask mAP of 0.887, and
an inference speed of 47.3 FPS on the 2018 Data Science Bowl dataset,
outperforming the state-of-the-art methods. The source code is available at
https://github.com/mkang315/ASF-YOLO.