Histopathology Image Classification using Deep Manifold Contrastive Learning
This work addresses a domain-specific problem for medical imaging by improving classification accuracy in histopathology, though it is incremental as it builds on existing contrastive learning techniques.
The authors tackled the problem of histopathology image classification by proposing a deep manifold contrastive learning method that uses geodesic distance instead of cosine distance for similarity measurement, resulting in improved performance over state-of-the-art methods on two real-world datasets.
Contrastive learning has gained popularity due to its robustness with good feature representation performance. However, cosine distance, the commonly used similarity metric in contrastive learning, is not well suited to represent the distance between two data points, especially on a nonlinear feature manifold. Inspired by manifold learning, we propose a novel extension of contrastive learning that leverages geodesic distance between features as a similarity metric for histopathology whole slide image classification. To reduce the computational overhead in manifold learning, we propose geodesic-distance-based feature clustering for efficient contrastive loss evaluation using prototypes without time-consuming pairwise feature similarity comparison. The efficacy of the proposed method is evaluated on two real-world histopathology image datasets. Results demonstrate that our method outperforms state-of-the-art cosine-distance-based contrastive learning methods.