VLM-CPL: Consensus Pseudo Labels from Vision-Language Models for Annotation-Free Pathological Image Classification

Understand

Classification of pathological images is the basis for automatic cancer diagnosis.

• Despite that deep learning methods have achieved remarkable performance, they heavily rely on labeled data, demanding extensive human annotation efforts.
• In this study, we present a novel human annotation-free method by leveraging pre-trained Vision-Language Models (VLMs).
• Without human annotation, pseudo-labels of the training set are obtained by utilizing the zero-shot inference capabilities of VLM, which may contain a lot of noise due to the domain gap between the pre-training and target datasets.