Detalhe da pesquisa
1.
The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights.
Radiology
; 310(2): e231319, 2024 Feb.
Artigo
em Inglês
| MEDLINE | ID: mdl-38319168
2.
The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping.
Radiology
; 295(2): 328-338, 2020 05.
Artigo
em Inglês
| MEDLINE | ID: mdl-32154773
3.
Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [82Rb] PET for MACE prediction.
Sci Rep
; 14(1): 9644, 2024 04 26.
Artigo
em Inglês
| MEDLINE | ID: mdl-38671059
4.
A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences.
Artif Intell Rev
; 56(4): 3473-3504, 2023.
Artigo
em Inglês
| MEDLINE | ID: mdl-36092822
5.
Automatic Head and Neck Tumor segmentation and outcome prediction relying on FDG-PET/CT images: Findings from the second edition of the HECKTOR challenge.
Med Image Anal
; 90: 102972, 2023 Dec.
Artigo
em Inglês
| MEDLINE | ID: mdl-37742374
6.
Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT.
Head Neck Tumor Chall (2022)
; 13626: 1-30, 2023.
Artigo
em Inglês
| MEDLINE | ID: mdl-37195050
7.
Automated Tumor Segmentation in Radiotherapy.
Semin Radiat Oncol
; 32(4): 319-329, 2022 10.
Artigo
em Inglês
| MEDLINE | ID: mdl-36202435
8.
Segmentation and Classification of Head and Neck Nodal Metastases and Primary Tumors in PET/CT.
Annu Int Conf IEEE Eng Med Biol Soc
; 2022: 4731-4735, 2022 07.
Artigo
em Inglês
| MEDLINE | ID: mdl-36086273
9.
Cleaning radiotherapy contours for radiomics studies, is it worth it? A head and neck cancer study.
Clin Transl Radiat Oncol
; 33: 153-158, 2022 Mar.
Artigo
em Inglês
| MEDLINE | ID: mdl-35243026
10.
Head and neck tumor segmentation in PET/CT: The HECKTOR challenge.
Med Image Anal
; 77: 102336, 2022 04.
Artigo
em Inglês
| MEDLINE | ID: mdl-35016077
11.
Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods.
J Pers Med
; 11(9)2021 Aug 27.
Artigo
em Inglês
| MEDLINE | ID: mdl-34575619
12.
Local rotation invariance in 3D CNNs.
Med Image Anal
; 65: 101756, 2020 10.
Artigo
em Inglês
| MEDLINE | ID: mdl-32623274
13.
Training Deep Neural Networks for Small and Highly Heterogeneous MRI Datasets for Cancer Grading.
Annu Int Conf IEEE Eng Med Biol Soc
; 2020: 1758-1761, 2020 07.
Artigo
em Inglês
| MEDLINE | ID: mdl-33018338
14.
Neural network training for cross-protocol radiomic feature standardization in computed tomography.
J Med Imaging (Bellingham)
; 6(2): 024008, 2019 Apr.
Artigo
em Inglês
| MEDLINE | ID: mdl-31205978
15.
Staining Invariant Features for Improving Generalization of Deep Convolutional Neural Networks in Computational Pathology.
Front Bioeng Biotechnol
; 7: 198, 2019.
Artigo
em Inglês
| MEDLINE | ID: mdl-31508414