Identification of veterinary and medically important blood parasites using contrastive loss-based self-supervised learning - Supasuta Busayakanon; Morakot Kaewthamasorn; Natchapon Pinetsuksai; Teerawat Tongloy; Santhad Chuwongin; Siridech Boonsang; Veerayuth Kittichai (Veterinary World, 2024)
Background and Aim: Zoonotic diseases caused by various blood parasites are important public health concerns that impact animals and humans worldwide. The traditional method of microscopic examination for parasite diagnosis is labor-intensive, time-consuming, and prone to variability among observers, necessitating highly skilled and experienced personnel. Therefore, an innovative approach is required to enhance the conventional method. This study aimed to develop a self-supervised learning (SSL) approach to identify zoonotic blood parasites from microscopic images, with an initial focus on parasite species classification.