In this competition, you’ll develop image-based algorithms to identify histologically confirmed skin cancer cases with single-lesion crops from 3D total body photos (TBP). The image quality resembles close-up smartphone photos, which are regularly submitted for telehealth purposes. Your binary classification algorithm could be used in settings without access to specialized care and improve triage for early skin cancer detection.
Skin cancer can be deadly if not caught early, but many populations lack specialized dermatologic care. Over the past several years, dermoscopy-based AI algorithms have been shown to benefit clinicians in diagnosing melanoma, basal cell, and squamous cell carcinoma. However, determining which individuals should see a clinician in the first place has great potential impact. Triaging applications have a significant potential to benefit underserved populations and improve early skin cancer detection, the key factor in long-term patient outcomes.
Dermatoscope images reveal morphologic features not visible to the naked eye, but these images are typically only captured in dermatology clinics. Algorithms that benefit people in primary care or non-clinical settings must be adept to evaluating lower quality images. This competition leverages 3D TBP to present a novel dataset of every single lesion from thousands of patients across three continents with images resembling cell phone photos.
This competition challenges you to develop AI algorithms that differentiate histologically-confirmed malignant skin lesions from benign lesions on a patient. Your work will help to improve early diagnosis and disease prognosis by extending the benefits of automated skin cancer detection to a broader population and settings.
Awards:-
$65,000 will be awarded to the Top 5 teams with the highest scores on the Kaggle Leaderboard at the conclusion of the competition:
- 1st Place – $20,000
- 2nd Place – $15,000
- 3rd Place – $10,000
- 4th Place – $10,000
- 5th Place – $10,000
Deadline:- 30-08-2024
