In the world of medical imaging, detecting standard planes in fetal ultrasounds is crucial. This process can be tricky due to the varying quality of data from different sources. The challenge is even greater when dealing with noisy labels and differing amounts of data from various clients. The goal is to improve the learning process in a way that respects privacy. One approach is to use a method called federated learning. This method allows multiple clients to work together without sharing their actual data.

The idea is to use prototypes from the largest dataset to clean up the noisy labels. This way, all clients can benefit from improved predictions. The focus is on refining the labels and enhancing the accuracy of the results. The key is to make sure that the privacy of the data is maintained throughout the process. This is important because medical data is sensitive and needs to be protected.

Federated learning is a smart way to handle this issue. It allows for collaboration without compromising privacy. By using prototypes from the largest dataset, the system can refine the noisy labels. This leads to better predictions for all clients involved. The method is designed to work efficiently even when the data sizes vary greatly. This makes it a practical solution for real-world applications.

The approach is all about finding a balance. On one hand, there is the need for accurate predictions. On the other hand, there is the need to protect privacy. The federated denoising framework aims to achieve both. It uses the strengths of the largest dataset to improve the overall learning process. This way, even clients with smaller datasets can benefit from the collective knowledge. The method is a step forward in making fetal ultrasound detection more reliable and private.

Fetal Ultrasound: A Privacy-Focused Learning Approach

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