Graph-Based Multiple Instance Learning Enhances Skin Lesion Diagnosis
A recent study available on arXiv (2608.06037) introduces a two-tier relational framework aimed at enhancing image classification, with a focus on diagnosing skin lesions. The investigation starts with an EfficientNetB3 baseline and utilizes a patch-based approach through a convolutional masked autoencoder to uncover implicit inter-patch connections via self-supervised reconstruction. To add explicit relational modeling, the resulting embeddings are structured into different graph configurations, such as grid-based, random, and k-nearest neighbor. Tests conducted on the ISIC-2018 and ISIC-2019 datasets reveal that merging implicit inter-patch modeling with explicit graph-based message passing achieves optimal results. The research underscores the significance of relational inductive biases in understanding structural dependencies, suggesting that combining both implicit and explicit biases can greatly enhance diagnostic precision in medical imaging.
Key facts
- The study is published on arXiv with ID 2608.06037.
- It uses EfficientNetB3 as the baseline architecture.
- A convolutional masked autoencoder is used for implicit inter-patch relationship learning.
- Graph topologies include grid-based, random, and k-nearest neighbor structures.
- Experiments are conducted on ISIC-2018 and ISIC-2019 benchmarks.
- Combining implicit and explicit relational modeling yields the best results.
- The framework aims to bridge implicit representation learning and explicit structural modeling.
- Relational inductive biases are essential for capturing structural dependencies.
Entities
Institutions
- arXiv