Examining the Capability of GANs to Replace Real Biomedical Images in Classification Models Training

no code implementations • 18 Apr 2019 • Vassili Kovalev, Siarhei Kazlouski

The possibility of the use of artificial images instead of real ones for training machine learning models was examined by benchmark classification tasks being solved using conventional and deep learning methods.

BIG-bench Machine Learning General Classification

Influence of Control Parameters and the Size of Biomedical Image Datasets on the Success of Adversarial Attacks

no code implementations • 15 Apr 2019 • Vassili Kovalev, Dmitry Voynov

We concluded that: (1) An increase of the amplitude of perturbation in generating malicious adversarial images leads to a growth of the fraction of successful attacks for the majority of image types examined in this study.

Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge

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.

Mitosis Detection whole slide images