More than 3000 drug synthetic routes.
Over 1000 drug-target interactions.
Over 20,000 annotated CT images for cancer detection.
DICOM (Digital Imaging and Communications in Medicine) format standardizes imaging data while retaining high resolution, critical metadata, ensuring accuracy and interoperability.
Supporting online and active learning models enables your platform to adapt continuously and refine performance based on new data, directly benefiting applications requiring real-time adjustments and personalized insights.
Real-time monitoring in data labeling ensures timely feedback and quality control, enabling rapid identification and correction of errors.
1. Detect nodules or abnormalities in lung CT scans to diagnose early signs of lung cancer with pre-trained CNN model like ResNet or VGG.
2. Segment brain tumors from CT scans to identify the affected regions with U-Net.
3. Automatically detect COVID-19 pneumonia from chest CT images with EfficientNet or InceptionV3.
1. Predict molecular properties such as solubility, toxicity, or binding affinity with a SMILES string
AuroraGPT: With one trillion parameters, AuroraGPT (also referred to as "ScienceGPT") is expected to assist researchers in fields like biology, climate science, and cancer research by streamlining data analysis and providing insights through a chatbot interface.
1. The ChatGPT-o1 may leverage reinforcement learning to enhance its ability for logical reasoning.
2. Reinforcement learning demonstrates outstanding performance in managing dynamic treatment plans for chronic diseases and critical care.
3. Reinforcement learning is applied in automated medical diagnosis by utilizing both unstructured and structured clinical data.
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