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This paper presents a deep Bayesian active learning-to-rank method for estimating the severity of ulcerative colitis using relative annotations, which simplifies the annotation process by comparing pairs of images rather than assigning discrete severity labels. The proposed method leverages Bayesian convolutional neural networks to efficiently select high-uncertainty image pairs for annotation, thus improving performance while addressing class imbalances in medical image datasets. Experimental results demon

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philoklein222

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EN