1 code implementation • 21 Jan 2025 • Mohamed Harmanani, Amoon Jamzad, Minh Nguyen Nhat To, Paul F. R. Wilson, Zhuoxin Guo, Fahimeh Fooladgar, Samira Sojoudi, Mahdi Gilany, Silvia Chang, Peter Black, Michael Leveridge, Robert Siemens, Purang Abolmaesumi, Parvin Mousavi
However, prostate ultrasound images lack pixel-level cancer annotations, introducing label noise.
no code implementations • 17 Jul 2024 • Mahdi Gilany, Mohamed Harmanani, Paul Wilson, Minh Nguyen Nhat To, Amoon Jamzad, Fahimeh Fooladgar, Brian Wodlinger, Purang Abolmaesumi, Parvin Mousavi
This distribution shift significantly impacts the model's robustness, posing major challenge to clinical deployment.
no code implementations • 27 Mar 2024 • Mohamed Harmanani, Paul F. R. Wilson, Fahimeh Fooladgar, Amoon Jamzad, Mahdi Gilany, Minh Nguyen Nhat To, Brian Wodlinger, Purang Abolmaesumi, Parvin Mousavi
In this work, we present a detailed study of several image transformer architectures for both ROI-scale and multi-scale classification, and a comparison of the performance of CNNs and transformers for ultrasound-based prostate cancer classification.
1 code implementation • 13 Aug 2023 • Fahimeh Fooladgar, Minh Nguyen Nhat To, Parvin Mousavi, Purang Abolmaesumi
However, their performance degrades when training data contains noisy labels, leading to poor generalization on the test set.
1 code implementation • 3 Mar 2023 • Mahdi Gilany, Paul Wilson, Andrea Perera-Ortega, Amoon Jamzad, Minh Nguyen Nhat To, Fahimeh Fooladgar, Brian Wodlinger, Purang Abolmaesumi, Parvin Mousavi
We analyze this method using a dataset of micro-ultrasound acquired from 578 patients who underwent prostate biopsy, and compare our model to baseline models and other large-scale studies in the literature.
no code implementations • 1 Nov 2022 • Paul F. R. Wilson, Mahdi Gilany, Amoon Jamzad, Fahimeh Fooladgar, Minh Nguyen Nhat To, Brian Wodlinger, Purang Abolmaesumi, Parvin Mousavi
Our method outperforms baseline supervised learning approaches, generalizes well between different data centers, and scale well in performance as more unlabeled data are added, making it a promising approach for future research using large volumes of unlabeled data.
no code implementations • 21 Jul 2022 • Mahdi Gilany, Paul Wilson, Amoon Jamzad, Fahimeh Fooladgar, Minh Nguyen Nhat To, Brian Wodlinger, Purang Abolmaesumi, Parvin Mousavi
We train a deep model using a co-teaching paradigm to handle noise in labels, together with an evidential deep learning method for uncertainty estimation.
no code implementations • 31 Oct 2018 • Quoc Dang Vu, Simon Graham, Minh Nguyen Nhat To, Muhammad Shaban, Talha Qaiser, Navid Alemi Koohbanani, Syed Ali Khurram, Tahsin Kurc, Keyvan Farahani, Tianhao Zhao, Rajarsi Gupta, Jin Tae Kwak, Nasir Rajpoot, Joel Saltz
Segmentation of nuclei and classification of tissue images are two common tasks in tissue image analysis.
no code implementations • 13 Aug 2018 • Guilherme Aresta, Teresa Araújo, Scotty Kwok, Sai Saketh Chennamsetty, Mohammed Safwan, Varghese Alex, Bahram Marami, Marcel Prastawa, Monica Chan, Michael Donovan, Gerardo Fernandez, Jack Zeineh, Matthias Kohl, Christoph Walz, Florian Ludwig, Stefan Braunewell, Maximilian Baust, Quoc Dang Vu, Minh Nguyen Nhat To, Eal Kim, Jin Tae Kwak, Sameh Galal, Veronica Sanchez-Freire, Nadia Brancati, Maria Frucci, Daniel Riccio, Yaqi Wang, Lingling Sun, Kaiqiang Ma, Jiannan Fang, Ismael Kone, Lahsen Boulmane, Aurélio Campilho, Catarina Eloy, António Polónia, Paulo Aguiar
From the submitted algorithms it was possible to push forward the state-of-the-art in terms of accuracy (87%) in automatic classification of breast cancer with histopathological images.