Toward Robust Canine Cardiac Diagnosis: Deep Prototype Alignment Network-Based Few-Shot Segmentation in Veterinary Medicine

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.

DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep Learning Framework

Childhood and adolescent obesity rates are a global concern because obesity is associated with chronic diseases and long-term health risks. Artificial intelligence technology has emerged as a promising solution to accurately predict obesity rates and provide personalized feedback to adolescents. This study emphasizes the importance of early identification and prevention of obesity-related health issues. Factors such as height, weight, waist circumference, calorie intake, physical activity levels, and other relevant health information need to be considered for developing robust algorithms for obesity rate prediction and delivering personalized feedback. Hence, by collecting health datasets from 321 adolescents, we proposed an adolescent obesity prediction system that provides personalized predictions and assists individuals in making informed health decisions. Our proposed deep learning framework, DeepHealthNet, effectively trains the model using data augmentation techniques, even when daily health data are limited, resulting in improved prediction accuracy (acc: 0.8842). Additionally, the study revealed variations in the prediction of the obesity rate between boys (acc: 0.9320) and girls (acc: 0.9163), allowing the identification of disparities and the determination of the optimal time to provide feedback. The proposed system shows significant potential in effectively addressing childhood and adolescent obesity.

Multi-view Cross-Modality MR Image Translation for Vestibular Schwannoma and Cochlea Segmentation

In this work, we propose a multi-view image translation framework, which can translate contrast-enhanced T1 (ceT1) MR imaging to high-resolution T2 (hrT2) MR imaging for unsupervised vestibular schwannoma and cochlea segmentation. We adopt two image translation models in parallel that use a pixel-level consistent constraint and a patch-level contrastive constraint, respectively. Thereby, we can augment pseudo-hrT2 images reflecting different perspectives, which eventually lead to a high-performing segmentation model. Our experimental results on the CrossMoDA challenge show that the proposed method achieved enhanced performance on the vestibular schwannoma and cochlea segmentation.