Automated Segmentation: Utilize Med-ImageNet data to train models that
can perform automatic segmentation of tumors and relevant structures within
medical images, reducing the time and effort required for manual labeling.
Annotation Accuracy: Ensure that auto-segmentation tools produce precise
and clinically relevant boundaries, enhancing the quality of downstream AI
applications.
Model Validation: Implement rigorous validation processes to assess the
reliability and accuracy of segmentation models, making them suitable for
clinical and research applications.
AI-driven Planning: Enable AI models trained on Med-ImageNet data to
support personalized treatment planning by identifying key anatomical
features and potential risks.
Patient Monitoring: Leverage imaging data to monitor treatment progress
and detect early signs of relapse or treatment response, enabling timely
interventions.
Outcome Prediction: Develop models that use imaging features to predict
patient outcomes, supporting oncologists in making data-driven treatment
decisions.
Challenge Objective: Host a public machine learning challenge focused on
head and neck cancer segmentation and analysis to encourage innovation and
improve model accuracy.
Data Access and Submission: Provide participants with access to
anonymized Med-ImageNet data for training and testing, with submissions
evaluated on segmentation accuracy and clinical relevance.
Advancing Model Generalizability: Use the challenge to identify models
that generalize well across diverse data, with high potential for clinical
implementation.