Jovana Panic
Orcid: 0000-0002-6620-5610
According to our database1,
Jovana Panic
authored at least 10 papers
between 2020 and 2023.
Collaborative distances:
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Bibliography
2023
Comparison between Different Approaches for the Creation of the Training Set: How Clustering and Dimensionality Impact the Performance of a Deep Learning Model.
Proceedings of the 23rd IEEE International Conference on Bioinformatics and Bioengineering, 2023
2022
Impact of network parameters on a U-Net based system for rectal cancer segmentation on MR images.
Proceedings of the IEEE International Symposium on Medical Measurements and Applications, 2022
A Deep Learning model to segment liver metastases on CT images acquired at different time-points during chemotherapy.
Proceedings of the IEEE International Symposium on Medical Measurements and Applications, 2022
A fully automatic deep learning algorithm to segment rectal Cancer on MR images: a multi-center study.
Proceedings of the 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2022
2021
Comparison of radiomics approaches to predict resistance to 1st line chemotherapy in liver metastatic colorectal cancer.
Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2021
Deep learning model for automatic prostate segmentation on bicentric T2w images with and without endorectal coil.
Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2021
2020
Proceedings of the 2020 IEEE International Symposium on Medical Measurements and Applications, 2020
Deep learning to segment liver metastases on CT images: impact on a radiomics method to predict response to chemotherapy.
Proceedings of the 2020 IEEE International Symposium on Medical Measurements and Applications, 2020
A Convolutional Neural Network based system for Colorectal cancer segmentation on MRI images.
Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2020
An innovative radiomics approach to predict response to chemotherapy of liver metastases based on CT images.
Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2020