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Integrated data and machine learning transform lung cancer diagnosis and treatment - Bioengineer.org
Integrated data and machine learning transform lung cancer diagnosis and treatment Bioengineer.org
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- PossiblePossibly related (embedding) · 54%CT-CLIP Representations for Multimodal Lung Cancer Survival Prediction →
- PossiblePossibly related (embedding) · 54%Can Unsupervised Methods Outperform Supervised Deep Learning When Ground Truth Is Sparse? A Case Study of Bronchovascular Bundle Segmentation in Low-Dose CT →
- PossiblePossibly related (embedding) · 53%Foundation Models vs. Radiomics for Lung Computed Tomography: A Benchmark of Feature Extractors, Classification Heads, and Segmentation Choices →
- PossiblePossibly related (embedding) · 53%CRC-HGD: A Histopathological Image Dataset for Grading Colorectal Cancer →
- PossiblePossibly related (embedding) · 52%GigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment Analysis →
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paperCT-CLIP Representations for Multimodal Lung Cancer Survival PredictionpaperCan Unsupervised Methods Outperform Supervised Deep Learning When Ground Truth Is Sparse? A Case Study of Bronchovascular Bundle Segmentation in Low-Dose CTpaperFoundation Models vs. Radiomics for Lung Computed Tomography: A Benchmark of Feature Extractors, Classification Heads, and Segmentation ChoicespaperCRC-HGD: A Histopathological Image Dataset for Grading Colorectal CancerpaperGigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment Analysis
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paperCT-CLIP Representations for Multimodal Lung Cancer Survival PredictionpaperCRC-HGD: A Histopathological Image Dataset for Grading Colorectal CancerpaperGigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment AnalysispaperCan Unsupervised Methods Outperform Supervised Deep Learning When Ground Truth Is Sparse? A Case Study of Bronchovascular Bundle Segmentation in Low-Dose CTpaperFoundation Models vs. Radiomics for Lung Computed Tomography: A Benchmark of Feature Extractors, Classification Heads, and Segmentation Choices