Francesco Sardanelli
Genetics · 2010–2021
A researcher in the Science corpus: 4 papers; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Genetics — 7 citations into it
- Statistics and Probability — 7 citations into it
- Economics and Econometrics — 5 citations into it
Whom Francesco Sardanelli trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
Papers from Francesco Sardanelli's perspective
Ordered by how strongly the researchers Francesco Sardanelli trusts endorse each paper. Same corpus, a different order for every researcher.
- Automatic Segmentation of MR Brain Images With a Convolutional Neural Network (IEEE Transactions on Medical Imaging)
- Why Most Published Research Findings Are False
- Estimation of the multiple testing burden for genomewide association studies of nearly all common variants (Genetic Epidemiology)
- Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans (Scientific Reports)
- Low-dose CT via convolutional neural network. (Biomedical Optics Express)
- Radiomics: the bridge between medical imaging and personalized medicine (Nature Reviews Clinical Oncology)
- The ASA Statement on p-Values: Context, Process, and Purpose
- Contralateral breast cancer in BRCA1 and BRCA2 mutation carriers. (Journal of Clinical Oncology)
- Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? (Insights into Imaging)
- Radiomics: Images Are More than Pictures, They Are Data (Radiology)
Their papers
- AI applications to medical images: From machine learning to deep learning. (2021) — 612 citations
- Statistical significance: p value, 0.05 threshold, and applications to radiomics—reasons for a conservative approach (2020) — 416 citations
- Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine (2018) — 705 citations
- Magnetic resonance imaging of the breast: recommendations from the EUSOMA working group. (2010) — 954 citations