Research And Implementation Of HIV Brain Image Classification Algorithm Based On Federated Learning

In the pre infection stage of Acquired Human Immunodeficiency Syndrome(HIV),even with treatment,infected individuals have a nearly 50%chance of developing cognitive,sensory,and motor dysfunction,known as HIV Associated Neurocognitive Disorders(HAND).At present,the diagnosis and treatment of HAND in clinical practice mainly focus on the third stage,where neurons have already experienced inevitable damage;In the first stage of HAND(ANI),even if there have been neurological changes in HAND,it is often difficult to distinguish due to the absence of clinical symptoms or mild symptoms,thus missing the golden period for targeted prevention and intervention,and even delaying or reversing the pathological process of HAND.In order to detect the patient’s symptoms as early as possible and complete the diagnosis in the first stage without significant changes,it is possible to consider using medical image post-processing technology to fully utilize the value of existing medical images and combine computer vision technology to achieve "early detection and early treatment".In current computer vision tasks,solutions represented by deep learning can be used to assist or even replace doctors in decision-making,and specific classification tasks can be achieved based on medical image data represented by CT and MRI,such as completing the diagnosis of the clinical stage of HAND in the current infected person.However,medical image classification algorithms based on artificial intelligence technology heavily rely on high-quality annotated data for supervised training,and currently most medical data is limited to various hospitals and related institutions.In addition,the number of medical data samples for HIV,a special disease,is small and not centrally distributed,and has very strict privacy requirements.Therefore,relying solely on a single institution to process medical data is greatly limited,which also makes traditional deep learning methods appear mediocre in this scenario.To solve the above problems,this paper proposes a pre clinical ANI diagnostic classification method of HAND based on federal learning.The independent medical institutions participating in federal learning use the model related parameters after homomorphic encryption for joint modeling,and the coordinator flexibly adjusts the impact of each institution on the entire model by adjusting the aggregation weight,Under the requirement of ensuring that the data only flows within its original acquisition range,fully utilize the originally isolated sample data,integrate resources from all parties(hospitals),and ultimately train a global model with higher accuracy to assist doctors in identifying pre clinical patients with HAND.Under the effect of the additive homomorphic encryption algorithm,the encrypted parameters can be aggregated directly without decryption,breaking away from the restriction of the privacy of the original medical sample data,and realizing the "available and invisible" of the data;By adjusting the aggregation weights of parameters to address the practical problem of uneven sample distribution,engineering experiments were conducted on the HAND dataset provided by Beijing You’an Hospital affiliated with Capital Medical University for comparative verification.The performance indicators of the model trained using federated learning and traditional deep learning were fully compared.Finally,it was concluded that the proposed federated learning method in this paper can achieve an effect similar to a single institution mastering all annotated data.