Toward Robust Canine Cardiac Diagnosis: Deep Prototype Alignment
  Network-Based Few-Shot Segmentation in Veterinary Medicine
Jun-Young Oh In-Gyu Lee Tae-Eui Kam Ji-Hoon Jeong
In the cutting-edge domain of medical artificial intelligence (AI),
remarkable advances have been achieved in areas such as diagnosis, prediction,
and therapeutic interventions. Despite these advances, the technology for image
segmentation faces the significant barrier of having to produce extensively
annotated datasets. To address this challenge, few-shot segmentation (FSS) has
been recognized as one of the innovative solutions. Although most of the FSS
research has focused on human health care, its application in veterinary
medicine, particularly for pet care, remains largely limited. This study has
focused on accurate segmentation of the heart and left atrial enlargement on
canine chest radiographs using the proposed deep prototype alignment network
(DPANet). The PANet architecture is adopted as the backbone model, and
experiments are conducted using various encoders based on VGG-19, ResNet-18,
and ResNet-50 to extract features. Experimental results demonstrate that the
proposed DPANet achieves the highest performance. In the 2way-1shot scenario,
it achieves the highest intersection over union (IoU) value of 0.6966, and in
the 2way-5shot scenario, it achieves the highest IoU value of 0.797. The DPANet
not only signifies a performance improvement, but also shows an improved
training speed in the 2way-5shot scenario. These results highlight our model's
exceptional capability as a trailblazing solution for segmenting the heart and
left atrial enlargement in veterinary applications through FSS, setting a new
benchmark in veterinary AI research, and demonstrating its superior potential
to veterinary medicine advances.