Data is the fuel of deep learning. Its power and promise highly depend on the amount and quality of training data. Medical data are abundant but not accessible mostly due to data privacy regulations.
WisdomX addresses this inaccessibility challenge by means of its Federated Learning system tailored to the training of mammography interpretation and analysis models.
Data is the fuel of deep learning. Its power and promise highly depend on the amount and quality of training data. Medical data are abundant but not accessible mostly due to data privacy regulations.
WisdomX addresses this inaccessibility challenge by means of its Federated Learning system tailored to the training of mammography interpretation and analysis models.
Data is the fuel of deep learning. Its power and promise highly depend on the amount and quality of training data. Medical data are abundant but not accessible mostly due to data privacy regulations.
WisdomX addresses this inaccessibility challenge by means of its Federated Learning system tailored to the training of mammography interpretation and analysis models.
This system distributes copies of a deep learning model to the sites where the data is kept. At this sites training iterations are performed locally and results of the computation are returned to the central server of WisdomX to update the main model.
This system distributes copies of a deep learning model to the sites where the data is kept. At this sites training iterations are performed locally and results of the computation are returned to the central server of WisdomX to update the main model.
WisdomX’s Federated Learning system seamlessly enables:
• Sharing of wisdom not data
• Retention of data sovereignty
• Data privacy protection
WisdomX’s Federated Learning system seamlessly enables:
• Sharing of wisdom not data
• Retention of data sovereignty
• Data privacy protection
WisdomX has devised its own proprietary data valuation and data contributor incentivization mechanism based on the Shapley value concept. For each client that participates in the federated learning, a parameter called Client Contribution Index (CCI) is calculated based on its all contributed images and their respective values. The CCI associated with each client will determine how much incentive this client will receive from WisdomX.
WisdomX has devised its own proprietary data valuation and data contributor incentivization mechanism based on the Shapley value concept. For each client that participates in the federated learning, a parameter called Client Contribution Index (CCI) is calculated based on its all contributed images and their respective values. The CCI associated with each client will determine how much incentive this client will receive from WisdomX.
WisdomX has devised its own proprietary data valuation and data contributor incentivization mechanism based on the Shapley value concept. For each client that participates in the federated learning, a parameter called Client Contribution Index (CCI) is calculated based on its all contributed images and their respective values. The CCI associated with each client will determine how much incentive this client will receive from WisdomX.