Open Access Journal of Applied Science and Technology(OAJAST)

Researcher Linnear Austin, USA

Publications

• Review Article   
  Multi-Head Automated Segmentation by Incorporating Detection Head into the Contextual Layer Neural Network
  Author(s): Edwin Kys and Febian Febian*
  
  Deep-learning–based auto-segmentation is increasingly used in radiotherapy, but conventional models often produce anatomically implausible false positives, or “hallucinations,” in slices lacking target structures. We propose a gated multi-head Transformer architecture based on Swin U-Net, augmented with inter-slice context integration and a parallel detection head, which jointly performs slice-level structure detection via a multi-layer perceptron and pixel-level segmentation through a context-enhanced stream. Detection outputs gate the segmentation predictions to suppress false positives in anatomically invalid slices, and training uses slice-wise Tversky loss to address class imbalance. Experiments on the ProstateAnatomical-Edge-Cases dataset from The Cancer Imaging Archive demonstrate that the gated model substantially outperforms a non-gated segmentation-only bas.. Read More»
  

  Abstract HTML PDF