Precise and robust image segmentation is one of the most important steps in supervised deep learning-applied studies. Especially in the medical field, image segmentation requires an enormous time and professionals with clinical knowledge. Although there are constant attempts for automatic and semi-automatic image segmentation algorithm development, acquiring not only clinically accurate but also precise pixel-level annotations for medical images remains insufficient. This article presents a semi-supervised learning method with a novel fact-forcing process, referred to as FFSS, to reduce the labeling cost while improving the prediction accuracy for medical image segmentation. FFSS includes two components: a pre-trained teacher and a student that would be trained, iteratively. In each iteration, the teacher first generates a pseudo-label for each image in an unlabeled set, the student is then trained on the pseudo-labeled set and sends feedback to update the teacher. A fact-forcing process is designed to improve the quality of the student model using a labeled set. We have comprehensively evaluated our method on both three-dimensional binary segmentation and two-dimensional multi-class segmentation. The evaluation results demonstrate significant accuracy improvements of FFSS compared with the state-of-the-art semi-supervised methods. Due to the fact-forcing process, the proposed method consistently outperforms the other ones under various labeled data ratios for all benchmark datasets, including left atrium MRI, pancreas CT, ACDC MRI, and OCT. By refining the quality of student feedback with complementary supervised training, the proposed FFSS shows robustness under labeled data scarcity for diverse types of medical images.

In recent decades, medical imaging techniques have revolutionized the field of disease diagnosis, enabling healthcare professionals to noninvasively observe the internal structures of the human body. Among these techniques, optical coherence tomography (OCT) has emerged as a powerful and versatile tool that allows high-resolution, non-invasive, and real-time imaging of biological tissues. Deep learning algorithms have been successfully employed to detect and classify various retinal diseases in OCT images, enabling early diagnosis and treatment planning. However, existing deep learning algorithms are primarily designed for single-disease diagnosis, which limits their practical application in clinical settings where OCT images often contain symptoms of multiple diseases. In this paper, we propose an effective approach for multi-disease diagnosis in OCT images using a multi-scale learning (MSL) method and a sparse residual network (SRN). Specifically, the MSL method extracts and fuses useful features from images of different sizes to enhance the discriminative capability of a classifier and make the disease predictions interpretable. The SRN is a minimal residual network, where convolutional layers with large kernel sizes are replaced with multiple convolutional layers that have smaller kernel sizes, thereby reducing model complexity while achieving a performance similar to that of existing convolutional neural networks. The proposed multi-scale sparse residual network significantly outperforms existing methods, exhibiting 97.40% accuracy, 95.38% sensitivity, and 98.25% specificity. Experimental results show the potential of our method to improve explainable diagnosis systems for various eye diseases via visual discrimination.