eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2025-02-20 18 March Spl Edition 59 72 10.13005/bpj/3073 64344 article Abdulahi Mahammed Adem 1 Ravi Kant 1 Sonia 2 Karan Kumar 4 Vikas Mittal 5 Pankaj Jain 6 School of Core Engineering, Shoolini University, Solan, Himachal Pradesh, India. Yogananda School of AI Computers and Data Science, Shoolini University, Solan, Himachal Pradesh, India Department of Electronics and Communication Engineering, MMEC, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, India Department of Electronics and Communication Engineering, Chandigarh University, Gharuan, Mohali-140413, India Department of Management, SJJTU University, Churu Rajasthan, India Department of Computer Science and Engineering, Uttaranchal Institute of Technology (UIT), Uttaranchal University, Dehradun, Uttarakhand, India In digital image processing for disease categorization and detection, the introduction of neural networks has played a significant role. However, the need for substantial labelled data brings a challenge which often limits its effectiveness in pathology image interpretation. This study explores self-supervised learning’s potential to overcome the constraints of labelled data by using unlabeled or unannotated data as a learning signal. This study also focuses on self-supervised learning application in digital pathology where images can reach gigapixel sizes, requiring meticulous scrutiny. Advancements in computational medicine have introduced tools processing vast pathological images by encoding them into tiles. The review also explores cutting-edge methodologies such as contrastive learning and context restoration within the domain of digital pathology. The primary focus of this study centers around self-supervised learning techniques, specially applied to disease detection and classification in digital pathology. The study addresses the challenges associated with less labelled data and underscores the significance of self-supervised learning in extracting meaning full features from unlabelled pathology images. Using techniques like Longitudinal Self-supervised learning, the study provides a comparative study with traditional supervised learning approaches. The finding will contribute valuable insights and techniques by bridging the gap between digital pathology and machine learning communities. https://biomedpharmajournal.org/vol18marchspledition/exploring-self-supervised-learning-for-disease-detection-and-classification-in-digital-pathology-a-review/ : Context restoration Contrastive learning Deep learning Digital Pathology Self-supervised learning