Mammography
Project overview
This project focuses on developing and refining AI models to analyze mammograms for the detection of BACS, benign masses, cancerous tissues, and normal anatomy. The project involves several stages, from data annotation to model deployment.
The project involves the annotation of mammogram images, focusing on the identification of BACS, benign masses, cancerous tissues, and normal anatomical structures.
project objectives
Automated Detection and Diagnosis:
By annotating mammogram images, AI models can learn to accurately identify various breast conditions. This capability aids radiologists in diagnosing issues more swiftly and with greater precision, leading to timely and personalized treatment plans.
Improved Patient Engagement:
With AI's rapid analysis of mammogram images, patients receive faster feedback regarding their breast health. This reduces anxiety and empowers patients with information, making it easier for doctors to explain findings and involve patients in their care decisions.
Preventive Healthcare:
AI's predictive capabilities in mammography help in identifying early signs of breast cancer or other conditions that might not be apparent even to experienced radiologists. This early detection supports preventive strategies, potentially reducing the severity and impact of breast diseases.
Research and Development:
Annotated mammogram data are invaluable for ongoing research in breast health. This data can be used to study disease progression, treatment outcomes, and the effectiveness of new therapies, driving innovation in breast cancer detection and treatment.
