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Sara P Oliveira

Showing results (1-10 of 13) with videos related to

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PLOS Digital Health|May 13, 2026
Decoding (digital) histopathology: The building blocks for computational researchersSalma Dammak, Alessandro Caputo, Diana Montezuma, et al.
NPJ Digital Medicine|July 2, 2025
H&E to IHC virtual staining methods in breast cancer: an overview and benchmarkingPascal Klöckner, José Teixeira, Diana Montezuma, et al.
Sensors (Basel, Switzerland)|January 10, 2018
A Regression Model for Predicting Shape Deformation after Breast Conserving SurgeryHooshiar Zolfagharnasab, Sílvia Bessa, Sara P Oliveira, et al.
Modern Pathology : an Official Journal of the United States and Canadian Academy of Pathology, Inc|February 14, 2023
Annotating for Artificial Intelligence Applications in Digital Pathology: A Practical Guide for Pathologists and ResearchersDiana Montezuma, Sara P Oliveira, Pedro C Neto, et al.
Cancers|May 28, 2022
iMIL4PATH: A Semi-Supervised Interpretable Approach for Colorectal Whole-Slide ImagesPedro C Neto, Sara P Oliveira, Diana Montezuma, et al.
Scientific Reports|July 14, 2021
CAD systems for colorectal cancer from WSI are still not ready for clinical acceptanceSara P Oliveira, Pedro C Neto, João Fraga, et al.
Scientific Reports|March 9, 2023
A CAD system for automatic dysplasia grading on H&E cervical whole-slide imagesSara P Oliveira, Diana Montezuma, Ana Moreira, et al.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|September 10, 2022
Quality Control in Digital Pathology: Automatic Fragment Detection and CountingTome Albuquerque, Ana Moreira, Beatriz Barros, et al.
The Journal of Pathology|February 25, 2026
What practicing pathologists and oncologists should know about the new computational pathology-based companion diagnostic toolsDiana Montezuma, Sara P Oliveira, Inti Zlobec, et al.
NPJ Precision Oncology|March 5, 2024
An interpretable machine learning system for colorectal cancer diagnosis from pathology slidesPedro C Neto, Diana Montezuma, Sara P Oliveira, et al.
Pageof 2

Showing results (1-10 of 13) with videos related to

Sort By:
Pageof 2
PLOS Digital Health|May 13, 2026
Decoding (digital) histopathology: The building blocks for computational researchersSalma Dammak, Alessandro Caputo, Diana Montezuma, et al.
NPJ Digital Medicine|July 2, 2025
H&E to IHC virtual staining methods in breast cancer: an overview and benchmarkingPascal Klöckner, José Teixeira, Diana Montezuma, et al.
Sensors (Basel, Switzerland)|January 10, 2018
A Regression Model for Predicting Shape Deformation after Breast Conserving SurgeryHooshiar Zolfagharnasab, Sílvia Bessa, Sara P Oliveira, et al.
Modern Pathology : an Official Journal of the United States and Canadian Academy of Pathology, Inc|February 14, 2023
Annotating for Artificial Intelligence Applications in Digital Pathology: A Practical Guide for Pathologists and ResearchersDiana Montezuma, Sara P Oliveira, Pedro C Neto, et al.
Cancers|May 28, 2022
iMIL4PATH: A Semi-Supervised Interpretable Approach for Colorectal Whole-Slide ImagesPedro C Neto, Sara P Oliveira, Diana Montezuma, et al.
Scientific Reports|July 14, 2021
CAD systems for colorectal cancer from WSI are still not ready for clinical acceptanceSara P Oliveira, Pedro C Neto, João Fraga, et al.
Scientific Reports|March 9, 2023
A CAD system for automatic dysplasia grading on H&E cervical whole-slide imagesSara P Oliveira, Diana Montezuma, Ana Moreira, et al.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|September 10, 2022
Quality Control in Digital Pathology: Automatic Fragment Detection and CountingTome Albuquerque, Ana Moreira, Beatriz Barros, et al.
The Journal of Pathology|February 25, 2026
What practicing pathologists and oncologists should know about the new computational pathology-based companion diagnostic toolsDiana Montezuma, Sara P Oliveira, Inti Zlobec, et al.
NPJ Precision Oncology|March 5, 2024
An interpretable machine learning system for colorectal cancer diagnosis from pathology slidesPedro C Neto, Diana Montezuma, Sara P Oliveira, et al.
Pageof 2