There has been a surge of research in deep learning models for WSI classification with clinical applications such as cancer detection or prediction of molecular mutations from WSIs.
1 code implementation • 20 Mar 2023 • Ricardo Mokhtari, Azam Hamidinekoo, Daniel Sutton, Arthur Lewis, Bastian Angermann, Ulf Gehrmann, Pal Lundin, Hibret Adissu, Junmei Cairns, Jessica Neisen, Emon Khan, Daniel Marks, Nia Khachapuridze, Talha Qaiser, Nikolay Burlutskiy
Finally, we identified several cell level features indicative of disease severity in CD and UC.
The second component is a hard attention classification model, which further extracts a sequence of multi-resolution glimpses from each tile for classification.
The proposed method incorporates context-aware consistency by contrasting pairs of overlapping images in a pixel-wise manner from changing contexts resulting in robust and context invariant features.
1 code implementation • 16 Jul 2021 • Talha Qaiser, Stefan Winzeck, Theodore Barfoot, Tara Barwick, Simon J. Doran, Martin F. Kaiser, Linda Wedlake, Nina Tunariu, Dow-Mu Koh, Christina Messiou, Andrea Rockall, Ben Glocker
To aid radiological reading, we propose an auxiliary task-based multiple instance learning approach (ATMIL) for MM classification with the ability to localize sites of disease.
In this worldwide spread of SARS-CoV-2 (COVID-19) infection, it is of utmost importance to detect the disease at an early stage especially in the hot spots of this epidemic.
The model is trained in multi-task learning manner with noise tolerant joint loss for classification localization and achieves better performance when given limited data in contrast to a simple deep model.
Estimating over-amplification of human epidermal growth factor receptor 2 (HER2) on invasive breast cancer (BC) is regarded as a significant predictive and prognostic marker.
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.
In this paper, we present a semi-supervised mitosis detection method which is designed to leverage a large number of unlabeled breast cancer WSIs.
no code implementations • 22 Jul 2018 • Mitko Veta, Yujing J. Heng, Nikolas Stathonikos, Babak Ehteshami Bejnordi, Francisco Beca, Thomas Wollmann, Karl Rohr, Manan A. Shah, Dayong Wang, Mikael Rousson, Martin Hedlund, David Tellez, Francesco Ciompi, Erwan Zerhouni, David Lanyi, Matheus Viana, Vassili Kovalev, Vitali Liauchuk, Hady Ahmady Phoulady, Talha Qaiser, Simon Graham, Nasir Rajpoot, Erik Sjöblom, Jesper Molin, Kyunghyun Paeng, Sangheum Hwang, Sunggyun Park, Zhipeng Jia, Eric I-Chao Chang, Yan Xu, Andrew H. Beck, Paul J. van Diest, Josien P. W. Pluim
The best performing automatic method for the first task achieved a quadratic-weighted Cohen's kappa score of $\kappa$ = 0. 567, 95% CI [0. 464, 0. 671] between the predicted scores and the ground truth.
In this work, we propose a tumor segmentation framework based on the novel concept of persistent homology profiles (PHPs).
no code implementations • 23 May 2017 • Talha Qaiser, Abhik Mukherjee, Chaitanya Reddy Pb, Sai Dileep Munugoti, Vamsi Tallam, Tomi Pitkäaho, Taina Lehtimäki, Thomas Naughton, Matt Berseth, Aníbal Pedraza, Ramakrishnan Mukundan, Matthew Smith, Abhir Bhalerao, Erik Rodner, Marcel Simon, Joachim Denzler, Chao-Hui Huang, Gloria Bueno, David Snead, Ian Ellis, Mohammad Ilyas, Nasir Rajpoot
In this paper, we report on a recent automated Her2 scoring contest, held in conjunction with the annual PathSoc meeting held in Nottingham in June 2016, aimed at systematically comparing and advancing the state-of-the-art Artificial Intelligence (AI) based automated methods for Her2 scoring.