Vasculitis – Inflammation of the Blood Vessels
This project focuses on vasculitis, a group of disorders characterized by inflammation of blood vessel walls, which can affect vessels of different sizes and lead to tissue ischemia and organ damage. In the skin, vasculitis often presents with palpable purpura, ulcers, or necrosis. The project explores the clinical and histopathological features of cutaneous vasculitis across various underlying systemic and localized diseases.
Mentor Details:
Prof. Iris Barshak
Mentor Details:
Requirments:
AI solution for vasculitis recognition.
Problem Statement
Vasculitis encompasses a broad spectrum of diseases with overlapping clinical and histological features, making diagnosis challenging. Accurate identification of vasculitis and its subtype is critical for appropriate treatment, as delayed or incorrect diagnosis may result in significant morbidity. Standardized approaches to recognizing vasculitic changes in tissue samples are essential to support consistent diagnosis.
Project Objectives
AI solution for vasculitis recognition.
Technical Scope
Image analysis
Object detection
Segmentation
Required Knowledge and Prerequisites
Core Requirements
Understanding of fundamental computer vision concepts
Experience with convolutional neural networks (CNNs)
Familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow)
Ability to work with image and video datasets
Recommended Background
Experience with OpenSlide and QuPath
Project Difficulty and Expected Level
Overall Difficulty: Medium
This project is well-suited for:
Teams of 1–3 students
This project can also be done coding free with the DeePathology STUDIO.
Expected Outcomes
Automated vasculitis detection