Pollen grain recognition using convolutional neural network. - Natalia Khanzhina; Evgeny Putin; Andrey Filchenkov; Elena Zamyatina (ESANN, 2018)

Synthetic images generation for text detection and recognition in the wild. - Natalia Khanzhina; Natalia Slepkova; Andrey Filchenkov (ICMV, 2019)

CT images GAN-based augmentation with AdaIN for lung nodules detection. - Natalia Khanzhina; Ilya Osmakov (ICMV, 2020)

Comparative Evaluation of Lung Cancer CT Image Synthesis with Generative Adversarial Networks. - Alexander Semiletov; Aleksandra Vatian; Maksim Krychkov; Natalia Khanzhina; Anton Klochkov; Aleksey Zubanenko; Roman Soldatov; Anatoly Shalyto 001; Natalia F. Gusarova (ICCS (3), 2021)

Monte Carlo Concrete DropPath for Epistemic Uncertainty Estimation in Brain Tumor Segmentation. - Natalia Khanzhina; Maxim Kashirin; Andrey Filchenkov (UNSURE/PIPPI@MICCAI, 2021)

Improving Multimodal Data Labeling with Deep Active Learning for Post Classification in Social Networks. - Dmitry Krylov; Semen Poliakov; Natalia Khanzhina; Alexey Zabashta; Andrey Filchenkov; Aleksandr Farseev (MULL @ ACM Multimedia, 2021)

Towards Robust Object Detection: Bayesian RetinaNet for Homoscedastic Aleatoric Uncertainty Modeling. - Natalia Khanzhina; Alexey Lapenok; Andrey Filchenkov (CoRR, 2021)

Combating data incompetence in pollen images detection and classification for pollinosis prevention. - Natalia Khanzhina; Andrey Filchenkov; Natalia Minaeva; Larisa Novoselova; Maxim V. Petukhov; Irina Kharisova; Julia Pinaeva; Georgiy Zamorin; Evgeny Putin; Elena Zamyatina; Anatoly Shalyto 001 (Comput. Biol. Medicine, 2022)

New Bayesian Focal Loss Targeting Aleatoric Uncertainty Estimate: Pollen Image Recognition. - Natalia Khanzhina; Maxim Kashirin; Andrey Filchenkov (CVPR Workshops, 2023)