Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning (2505.12074v2)

Abstract: Multiple Instance Learning is the predominant method for Whole Slide Image classification in digital pathology, enabling the use of slide-level labels to supervise model training. Although MIL eliminates the tedious fine-grained annotation process for supervised learning, whether it can learn accurate bag- and instance-level classifiers remains a question. To address the issue, instance-level classifiers and instance masks were incorporated to ground the prediction on supporting patches. These methods, while practically improving the performance of MIL methods, may potentially introduce noisy labels. We propose to bridge the gap between commonly used MIL and fully supervised learning by augmenting both the bag- and instance-level learning processes with pseudo-label correction capabilities elicited from weak to strong generalization techniques. The proposed algorithm improves the performance of dual-level MIL algorithms on both bag- and instance-level predictions. Experiments on public pathology datasets showcase the advantage of the proposed methods.

Insightful Overview of Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning

The paper "Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning" addresses critical challenges in the domain of Multiple Instance Learning (MIL), particularly in the application of whole slide image (WSI) classification used in computational pathology. MIL provides a mechanism for leveraging slide-level labels to guide model training, bypassing the need for detailed, instance-level annotations which are costly and time-consuming to obtain. This paper contributes to overcoming MIL limitations while enhancing predictive performance through a bi-directional mutual knowledge distillation framework.

Methodology and Contributions

The authors highlight the performance gaps between MIL and fully supervised learning frameworks, particularly concerning noisy labels potentially introduced by common MIL approaches. To mitigate these, they implement a dual-level training algorithm leveraging pseudo-label correction drawn from weak to strong generalization techniques. The proposed framework consists of two interconnected branches: an instance-level branch and a bag-level branch.

In the instance branch, pseudo-labels derived from the attention scores are used to guide the classifier training, thus refining the instance classification capability under weak supervision. The bag branch utilizes attention-based aggregation to generate bag-level predictions and integrates filtered instance predictions to aid in classifier training. This interplay between the branches fosters a mutually reinforcing learning environment wherein each component benefits from the other’s improved predictions.

Strong Numerical Results and Bold Claims

Experimental results showcase substantial improvements on public pathology datasets—specifically, CAMELYON16 and TCGA-NSCLC. The proposed method consistently outperforms existing MIL frameworks in both bag- and instance-level prediction tasks. Superior performance was noted particularly when compared with conventional techniques like ABMIL, DSMIL, and more advanced approaches such as CLAM and TransMIL. This reflects significant enhancements in classifier accuracy and AUC scores, emphasizing the model's strength in both conventional and cross-validation settings.

Implications and Future Directions

Practically, the advancements presented in this paper hold potential to significantly improve automated diagnostic systems in digital pathology, enabling more accurate and less resource-intensive analysis of medical imaging data. Theoretically, the integration of mutual distillation combined with weak-to-strong generalization techniques opens new avenues for refining MIL models further, possibly extending these principles to diverse weakly-supervised learning tasks across AI disciplines.

Future research directions might explore refining loss functions for enhanced generalization, examining alternative attention mechanisms, and devising more optimized scheduling strategies for dual-level learning. Furthermore, continued advancements in computational capabilities could allow further scaling of these models to accommodate larger datasets, thereby improving generalizability and robustness.

In conclusion, the paper sets forth an innovative approach to bridging the gap between MIL and fully supervised frameworks, leveraging pseudo-label correction capabilities to elevate both the practical application of MIL in pathology and its theoretical underpinnings in AI research. The demonstrated improvements in model performance indicate promising avenues for further research and application in complex visual recognition tasks.