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Develop an effective weakly supervised semantic segmentation framework for complex, multi-label histopathological whole-slide images by utilizing large language model feature guidance and uncertainty estimation.

September 5, 2024IEEE Journal of Biomedical and Health Informatics

Enhancing Weakly Supervised Semantic Segmentation With Multi-Label Contrastive Learning and LLM Features Guidance

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Develop an effective weakly supervised semantic segmentation framework for complex, multi-label histopathological whole-slide images by utilizing large language model feature guidance and uncertainty estimation.
Extracted class-specific embeddings from classifier weights and enriched them with large language model representations using dot-product attention fusion to support multi-label contrastive learning.
Implemented a robust learning module that assesses pseudo-label uncertainty across multi-layer features to filter out label noise during model training.
Evaluated segmentation performance on two benchmark histopathology datasets, specifically the LUAD and BCSS datasets.
Demonstrated superior segmentation performance on both the LUAD and BCSS histopathological image datasets relative to existing weakly supervised baselines.
Successfully mitigated the disruptive effects of noisy pseudo-labels by incorporating multi-layer uncertainty evaluations into the optimization process.

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Computational study demonstrates improved weakly supervised segmentation in histopathological whole-slide images, highlighting the utility of language model guidance and multi-label contrastive...

Cite This Study

Cai et al. (2024) studied this question.

synapsesocial.com/papers/6a7e9f2d769bdd359bcd9e3d https://doi.org/10.1109/jbhi.2024.3450013

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Implication

Computational study demonstrates improved weakly supervised segmentation in histopathological whole-slide images, highlighting the utility of language model guidance and multi-label contrastive...

Enhancing Weakly Supervised Semantic Segmentation With Multi-Label Contrastive Learning and LLM Features Guidance

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Discussion

Implication

Cite This Study

Enhancing Weakly Supervised Semantic Segmentation With Multi-Label Contrastive Learning and LLM Features Guidance

Key Points

Discussion

Implication

Cite This Study

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