  In recent years, significant progress has been made in the field of learning
from positive and unlabeled examples (PU learning), particularly in the context
of advancing image and text classification tasks. However, applying PU learning
to semi-supervised disease classification remains a formidable challenge,
primarily due to the limited availability of labeled medical images. In the
realm of medical image-aided diagnosis algorithms, numerous theoretical and
practical obstacles persist. The research on PU learning for medical
image-assisted diagnosis holds substantial importance, as it aims to reduce the
time spent by professional experts in classifying images. Unlike natural
images, medical images are typically accompanied by a scarcity of annotated
data, while an abundance of unlabeled cases exists. Addressing these
challenges, this paper introduces a novel generative model inspired by H\"older
divergence, specifically designed for semi-supervised disease classification
using positive and unlabeled medical image data. In this paper, we present a
comprehensive formulation of the problem and establish its theoretical
feasibility through rigorous mathematical analysis. To evaluate the
effectiveness of our proposed approach, we conduct extensive experiments on
five benchmark datasets commonly used in PU medical learning: BreastMNIST,
PneumoniaMNIST, BloodMNIST, OCTMNIST, and AMD. The experimental results clearly
demonstrate the superiority of our method over existing approaches based on KL
divergence. Notably, our approach achieves state-of-the-art performance on all
five disease classification benchmarks.
  By addressing the limitations imposed by limited labeled data and harnessing
the untapped potential of unlabeled medical images, our novel generative model
presents a promising direction for enhancing semi-supervised disease
classification in the field of medical image analysis.
