Mattea Welch
Relevante Arbeiten
Meistzitierte Publikationen im Bereich Gesundheit & MedTech
Radiomic Analysis: Study Design, Statistical Analysis, and Other Bias Mitigation Strategies
2022 · 76 Zit. · Radiology
Use of Response Permutation to Measure an Imaging Dataset’s Susceptibility to Overfitting by Selected Standard Analysis Pipelines
2024 · 4 Zit. · Academic Radiology
SCARF: Auto-Segmentation Clinical Acceptability & Reproducibility Framework for Benchmarking Essential Radiation Therapy Targets in Head and Neck Cancer
2022 · 2 Zit. · medRxiv
Med-ImageTools: An open-source Python package for robust data processing pipelines and curating medical imaging data
2023 · 2 Zit. · F1000Research
Med-ImageTools: An open-source Python package for robust data processing pipelines and curating medical imaging data
2024 · 2 Zit. · F1000Research
Abstract PR-04: A practical framework for operationalizing responsible and equitable AI in healthcare: Tackling bias, inequity, and implementation challenges
2025 · 1 Zit. · Clinical Cancer Research
Supplementary Data from Multi-institutional prognostic modelling in head and neck cancer:evaluating impact and generalizability of deep learning and radiomics
2023 · 0 Zit.
Supplementary Figure S2 from Multi-institutional prognostic modelling in head and neck cancer:evaluating impact and generalizability of deep learning and radiomics
2023 · 0 Zit.
TABLE 1 from Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics
2023 · 0 Zit.
Supplementary Figure S1 from Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics
2023 · 0 Zit.
Supplementary Figure S1 from Multi-institutional prognostic modelling in head and neck cancer:evaluating impact and generalizability of deep learning and radiomics
2023 · 0 Zit.
Supplementary Figure S3 from Multi-institutional prognostic modelling in head and neck cancer:evaluating impact and generalizability of deep learning and radiomics
2023 · 0 Zit.
Supplementary Data from Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics
2023 · 0 Zit.
Supplementary Figure S3 from Multi-institutional prognostic modelling in head and neck cancer:evaluating impact and generalizability of deep learning and radiomics
2023 · 0 Zit.
Supplementary Figure S4 from Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics
2023 · 0 Zit.