Semi-Supervised Disease Classification Based on Limited Medical Image Data

Inrecent 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 Hölder 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.

I. Introduction

Positive and unlabeled learning (PU learning) is a learning approach that aims to develop effective classifiers using only positive and unlabeled data, in contrast to traditional supervised learning (TSL) methods [1], [2], [3], [4], [5], [6]. PU learning poses unique challenges due to the limited availability of labeled data. To illustrate this point, let's consider the field of medical image processing, where the annotation of medical images requires the expertise of well-trained clinical professionals. However, these experts often face time constraints due to their busy schedules, making it challenging for them to manually review a large number of image samples. Consequently, the scarcity of labeled images hampers the development of effective classifiers.