arXiv Computer Vision
Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning
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arXiv Machine Learning
Oct 15
ProtoPur: Prototype-Purity Pseudo-Labeling for Weakly Supervised Instance Learning in Pathology
arXiv:2610.15669v2 Announce Type: replace-cross Abstract: Weak bag-level labels are widely available in computational pathology, whereas reliable instance-level annotations for whole-slide images rem...
arXiv Machine Learning
Oct 16
Beyond the Dominant Label: Recovering Coexisting Lesion Evidence with Multiple Instance Learning
The paper introduces DomSet-MIL, a multiple instance learning framework designed to recover evidence of coexisting lesions in whole-slide images when only the most severe diagnosis is weakly supervised. It models the dominant-label mechanism through a censored likelihood that treats the reported diagnosis as the maximum severity without assuming other lesions are absent, and employs severity-ordered residual routing with top‑q pooling to preserve secondary findings. Experiments on natural and controlled datasets show that DomSet-MIL outperforms standard MIL approaches in dominant-label prediction while better recovering latent coexisting-lesion evidence.
arXiv Computer Vision
Sep 28
LadderMIL: Multiple Instance Learning with Coarse-to-Fine Self-Distillation
arXiv:2502.02707v5 Announce Type: replace Abstract: Multiple Instance Learning (MIL) for whole slide image (WSI) analysis in computational pathology often neglects instance-level learning as supervis...
arXiv Machine Learning
Jun 8
LRMIL: Efficient Low-Resolution Multiple Instance Learning via High-Resolution Knowledge Distillation for Whole Slide Image Classification
arXiv:2606. 06864v1 Announce Type: cross Abstract: Multiple instance learning (MIL) has become a standard paradigm for whole slide image (WSI) analysis in digital pathology, as it enables slide-level prediction without dense annotations.
arXiv Machine Learning
Jun 5
In-Context Multiple Instance Learning
arXiv:2606. 06458v1 Announce Type: new Abstract: Multiple Instance Learning (MIL) addresses problems where supervision is available at the level of bags of instances and has been successfully applied in fields ranging from computational pathology to satellite imagery.
arXiv AI
Jul 17
Pretraining Multiple Instance Learning Networks with Multi-Teacher Distillation from Pathology Slide Foundation Models
arXiv:2607. 14703v1 Announce Type: cross Abstract: Multiple instance learning (MIL) has become the main paradigm for whole-slide image (WSI) analysis in computational pathology.