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Foundation models aid lung cancer growth pattern prediction with attention-based learning

By PulseAugur Editorial · [2 sources] ·

Researchers have developed an attention-based multiple instance learning (ABMIL) framework to predict lung adenocarcinoma growth patterns from whole slide images. This method reduces the need for extensive annotations by integrating pretrained pathology foundation models as patch encoders. Experiments demonstrated that fine-tuning these encoders improved performance, with Prov-GigaPath achieving a Kappa score of 0.699 under the ABMIL framework, outperforming simpler aggregation baselines. AI

IMPACT Introduces a novel framework for medical image analysis that reduces annotation burden and improves prediction accuracy for cancer growth patterns.

RANK_REASON Academic paper detailing a new framework for medical image analysis using foundation models.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Foundation models aid lung cancer growth pattern prediction with attention-based learning

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Academic paper detailing a new framework for medical image analysis using foundation models.
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COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Attention-based multiple instance learning for predominant growth pattern prediction in lung adenocarcinoma wsi using foundation models

    Lung adenocarcinoma (LUAD) grading depends on accurately identifying growth patterns, which are indicators of prognosis and can influence treatment decisions. Common deep learning approaches to determine the predominant pattern rely on patch-level classification or segmentation, …

  2. arXiv cs.CV TIER_1 English(EN) · Karen Lopez-Linares ·

    Attention-based multiple instance learning for predominant growth pattern prediction in lung adenocarcinoma wsi using foundation models

    Lung adenocarcinoma (LUAD) grading depends on accurately identifying growth patterns, which are indicators of prognosis and can influence treatment decisions. Common deep learning approaches to determine the predominant pattern rely on patch-level classification or segmentation, …

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