Related papers: Cross-patient Seizure Onset Zone Classification by Patient-Dependent Weight

Cross-patient Seizure Onset Zone Classification by Patient-Dependent Weight

URL: http://arxiv.org/abs/2508.03635v1
Date: Tue, 05 Aug 2025 16:50:50 GMT
Title: Cross-patient Seizure Onset Zone Classification by Patient-Dependent Weight
Authors: Xuyang Zhao, Hidenori Sugano, Toshihisa Tanaka,
Abstract summary: We propose a method to fine-tune a pretrained model using patient-specific weights for every new test patient to improve diagnostic performance.<n>Results show improved classification accuracy for every test patient, with an average improvement of more than 10%.
Score: 7.773508953474537
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Identifying the seizure onset zone (SOZ) in patients with focal epilepsy is essential for surgical treatment and remains challenging due to its dependence on visual judgment by clinical experts. The development of machine learning can assist in diagnosis and has made promising progress. However, unlike data in other fields, medical data is usually collected from individual patients, and each patient has different illnesses, physical conditions, and medical histories, which leads to differences in the distribution of each patient's data. This makes it difficult for a machine learning model to achieve consistently reliable performance in every new patient dataset, which we refer to as the "cross-patient problem." In this paper, we propose a method to fine-tune a pretrained model using patient-specific weights for every new test patient to improve diagnostic performance. First, the supervised learning method is used to train a machine learning model. Next, using the intermediate features of the trained model obtained through the test patient data, the similarity between the test patient data and each training patient's data is defined to determine the weight of each training patient to be used in the following fine-tuning. Finally, we fine-tune all parameters in the pretrained model with training data and patient weights. In the experiment, the leave-one-patient-out method is used to evaluate the proposed method, and the results show improved classification accuracy for every test patient, with an average improvement of more than 10%.

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