Recently, deep learning has been adopted to the glaucoma classification task with performance comparable to that of human experts . The proposed framework is adapted from the teacher-student-learning paradigm . The teacher model encodes the wrapped information of undiagnosed images to a latent feature space . Meanwhile, the student model learns from the . teacher through knowledge transfer to improve the . glau coma prediction performance. We propose a novel training strategy that simulates the real-world teaching practice named . ‘Learning To Teach with Knowledge Transfer (L2T-KT)’, and establish a ‘Quiz Pool’ as the teacher’s optimization target. Experiments show that the proposed framework was able to utilize the undi . data effectively to . improve the glauCOMA prediction performance to improve glauca prediction performance . The proposal is based on the .

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Keywords : teacher - learning - performance - student - glaucoma -

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