ASF-YOLO: A novel YOLO model with attentional scale sequence fusion for cell instance segmentation

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5 Citations (Scopus)

Abstract

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 multiscale information extraction capability of the network, and the Triple Feature Encoder (TFE) 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 TFE 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.

Original languageEnglish
Article number105057
Number of pages9
JournalImage and Vision Computing
Volume147
DOIs
Publication statusPublished - Jul 2024

Keywords

  • Attention mechanism
  • Medical image analysis
  • Sequence feature fusion
  • Small object segmentation
  • You only look once (YOLO)

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