Contacts
our Locations
Australia:
10 Leo Ave, Tarneit VIC 3029, Australia
Pakistan:
14, C Block, Street # 1, Model Town Phase 3, Multan Avenue, Multan
USA:
1910 Pacific Ave Suite 2000
PMB2461, Dallas, Texas 75201
contact Details:
DevVibe partnered with Air University to develop a Digital Twin–based system for monitoring and predictive maintenance of radiological equipment, improving healthcare infrastructure and operational efficiency.
DevVibe partnered with Air University to develop Gender-Neutral 8-Channel EMG Technology for Pelvic Floor Monitoring under NRPU funding.
DevVibe launched Enviro Era in collaboration with University of the Punjab to deliver environmental intelligence, climate technology, and AI-powered monitoring solutions.
Conferences
In this paper, a computationally effective and explainable Vision Transformer (ViT)-based framework is also suggested that can be used to perform automated classification of ultrasound images of the breast into benign, malignant, and normal issues. The pipeline combines paired image-mask representation, class-weighted training
Published in:
In this paper, a computationally effective and explainable Vision Transformer (ViT)-based framework is also suggested that can be used to perform automated classification of ultrasound images of the breast into benign, malignant, and normal issues. The pipeline combines paired image-mask representation, class-weighted training In this paper, a computationally effective and explainable Vision Transformer (ViT)-based framework is also suggested that can be used to perform automated classification of ultrasound images of the breast into benign, malignant, and normal issues. The pipeline combines paired image-mask representation, class-weighted training In this paper, a computationally effective and explainable Vision Transformer (ViT)-based framework is also suggested that can be used to perform automated classification of ultrasound images of the breast into benign, malignant, and normal issues. The pipeline combines paired image-mask representation, class-weighted training
