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TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| model instance correlations and spatial information with TransMIL | MIL for WSI classification · CAMELYON16 | AUC: 0.8880 -> 0.9309 | Helped |
| model instance correlations and spatial information with TransMIL | MIL for WSI classification · CAMELYON16 | accuracy: 0.8760 -> 0.8837 | Helped |
| model instance correlations and spatial information with TransMIL | MIL for WSI classification · TCGA-NSCLC | AUC: 0.9463 -> 0.9603 | Helped |
| model instance correlations and spatial information with TransMIL | MIL for WSI classification · TCGA-NSCLC | accuracy: 0.8619 -> 0.8835 | Helped |
| model instance correlations and spatial information with TransMIL | MIL for WSI classification · TCGA-RCC | AUC: 0.9879 -> 0.9882 | Helped |
| model instance correlations and spatial information with TransMIL | MIL for WSI classification · TCGA-RCC | accuracy: 0.9378 -> 0.9466 | Helped |
| add sinusoidal position encoding instead of no position encoding (ablation) | TransMIL · CAMELYON16 | AUC: 0.8416 -> 0.8941 | Helped |
| add sinusoidal position encoding instead of no position encoding (ablation) | TransMIL · TCGA-NSCLC | AUC: 0.9287 -> 0.9374 | Helped |
| add a 3×3 convolutional position encoding instead of none (ablation) | TransMIL · CAMELYON16 | AUC: 0.8416 -> 0.8913 | Helped |
| add a 3×3 convolutional position encoding instead of none (ablation) | TransMIL · TCGA-NSCLC | AUC: 0.9287 -> 0.9355 | Helped |
| add a 7×7 convolutional position encoding instead of none (ablation) | TransMIL · CAMELYON16 | AUC: 0.8416 -> 0.9015 | Helped |
| add a 7×7 convolutional position encoding instead of none (ablation) | TransMIL · TCGA-NSCLC | AUC: 0.9287 -> 0.9336 | Helped |
Closest papers by meaning
- CaMIL: Causal Multiple Instance Learning for Whole Slide Image Classification (AAAI 2024 · similarity 0.90)
- ViLa-MIL: Dual-scale Vision-Language Multiple Instance Learning for Whole Slide Image Classification (CVPR 2024 · similarity 0.88)
- Fast and Accurate Gigapixel Pathological Image Classification with Hierarchical Distillation Multi-Instance Learning (CVPR 2025 · similarity 0.87)
- Queryable Prototype Multiple Instance Learning with Vision-Language Models for Incremental Whole Slide Image Classification (AAAI 2025 · similarity 0.87)
- Transformer-Based Video-Structure Multi-Instance Learning for Whole Slide Image Classification (AAAI 2024 · similarity 0.86)
- Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classification (CVPR 2024 · similarity 0.86)
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