Kolmogorov-Arnold Convolutions: Design Principles and Empirical Studies

Autor: Drokin, Ivan

The emergence of Kolmogorov-Arnold Networks (KANs) has sparked significant interest and debate within the scientific community. This paper explores the application of KANs in the domain of computer vision (CV). We examine the convolutional version of

Externí odkaz: http://arxiv.org/abs/2407.01092

Deep Learning on Point Clouds for False Positive Reduction at Nodule Detection in Chest CT Scans

Autor: Drokin, Ivan, Ericheva, Elena

Publikováno v: In: van der Aalst W.M.P. et al. (eds) Analysis of Images, Social Networks and Texts. AIST 2020. Lecture Notes in Computer Science, vol 12602. Springer, Cham

This paper focuses on a novel approach for false-positive reduction (FPR) of nodule candidates in Computer-aided detection (CADe) systems following the suspicious lesions detection stage. Contrary to typical decisions in medical image analysis, the p

Externí odkaz: http://arxiv.org/abs/2005.03654

Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.

GANs 'N Lungs: improving pneumonia prediction

Autor: Malygina, Tatiana, Ericheva, Elena, Drokin, Ivan

We propose a novel method to improve deep learning model performance on highly-imbalanced tasks. The proposed method is based on CycleGAN to achieve balanced dataset. We show that data augmentation with GAN helps to improve accuracy of pneumonia bina

Externí odkaz: http://arxiv.org/abs/1908.00433

Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.