Artificial Intelligence in Healthcare: AI in Digital Pathology

Artificial Intelligence

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Level
Duration
Price
Certificate

All Levels
8 Weeks
6,300 AED
Bharat Academy Education

Certified

- KHDA
- AIAL
- Royal Institute for Chartered Engineer - RICE (USA)

Course Info

Key Highlights

Target Audience

Course Overview: This specialized course delves into the application of Artificial Intelligence in Medical Imaging for distinguishing between different types of lymphoma cancer. Explore how AI technologies are being used to enhance diagnostic accuracy, improve treatment planning, and reduce human error in the identification and classification of lymphoma subtypes. Gain hands-on experience with AI models tailored to the complex nature of medical images in oncology.

Capstone Project: Develop an AI model for distinguishing between different types of lymphoma using real medical imaging data. Present your findings and recommendations for potential clinical applications.

Course Format:

Duration: 8 weeks
Delivery: Onsite with interactive live sessions, lectures, and practical labs
Prerequisites: A basic understanding of AI and medical imaging
Tools Used: Python, TensorFlow and lymphoma-specific medical imaging datasets

Start your journey towards improving lymphoma diagnosis with AI, and make an impact in cancer treatment and care!

AI in Oncology: Learn how AI is transforming the diagnosis of lymphoma by automating the differentiation between Hodgkin's and non-Hodgkin's lymphoma, and further sub classifications.
Image Analysis and Preprocessing: Master techniques in preprocessing, enhancing image quality, and segmenting tumors in medical images, including PET, CT, and MRI scans.
Deep Learning for Cancer Detection: Understand the role of convolutional neural networks (CNNs) and other AI models in identifying key markers of lymphoma.
Lymphoma Case Studies: Work through real-world datasets and clinical scenarios to apply AI methods to lymphoma diagnosis, exploring success stories and challenges.
Accuracy and Sensitivity: Learn strategies to improve model accuracy, sensitivity, and specificity in differentiating between various lymphoma types.
Ethics and Clinical Integration: Address the ethical and regulatory aspects of using AI in cancer diagnostics, and discuss the integration of AI systems into clinical workflows.

Oncologists, radiologists, and healthcare professionals specializing in lymphoma
AI researchers and data scientists focused on cancer research
Healthcare startups developing AI solutions for oncology