Výsledky vyhledávání - "Fishman, Dmytro"

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Going Beyond U-Net: Assessing Vision Transformers for Semantic Segmentation in Microscopy Image Analysis

Autor: Tsiporenko, Illia, Chizhov, Pavel, Fishman, Dmytro

Segmentation is a crucial step in microscopy image analysis. Numerous approaches have been developed over the past years, ranging from classical segmentation algorithms to advanced deep learning models. While U-Net remains one of the most popular and

Externí odkaz: http://arxiv.org/abs/2409.16940

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COIN: Counterfactual inpainting for weakly supervised semantic segmentation for medical images

Autor: Shvetsov, Dmytro, Ariva, Joonas, Domnich, Marharyta, Vicente, Raul, Fishman, Dmytro

Publikováno v: In: Longo, L., Lapuschkin, S., Seifert, C. (eds) Explainable Artificial Intelligence. xAI 2024. Communications in Computer and Information Science, vol 2155. Springer, Cham

Deep learning is dramatically transforming the field of medical imaging and radiology, enabling the identification of pathologies in medical images, including computed tomography (CT) and X-ray scans. However, the performance of deep learning models,

Externí odkaz: http://arxiv.org/abs/2404.12832

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Metadata Improves Segmentation Through Multitasking Elicitation

Autor: Plutenko, Iaroslav, Papkov, Mikhail, Palo, Kaupo, Parts, Leopold, Fishman, Dmytro

Metainformation is a common companion to biomedical images. However, this potentially powerful additional source of signal from image acquisition has had limited use in deep learning methods, for semantic segmentation in particular. Here, we incorpor

Externí odkaz: http://arxiv.org/abs/2308.09411

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Developing a data analysis pipeline for automated protein profiling in immunology

Autor: Fishman, Dmytro

Accurate information about protein content in the organism is instrumental for a better understanding of human biology and disease mechanisms. While the presence of certain types of proteins can be life-threatening, the abundance of others is an esse

Externí odkaz: http://arxiv.org/abs/2201.06074

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Noise2Stack: Improving Image Restoration by Learning from Volumetric Data

Autor: Papkov, Mikhail, Roberts, Kenny, Madissoon, Lee Ann, Bayraktar, Omer, Fishman, Dmytro, Palo, Kaupo, Parts, Leopold

Biomedical images are noisy. The imaging equipment itself has physical limitations, and the consequent experimental trade-offs between signal-to-noise ratio, acquisition speed, and imaging depth exacerbate the problem. Denoising is, therefore, an ess

Externí odkaz: http://arxiv.org/abs/2011.05105

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DOME: Recommendations for supervised machine learning validation in biology

Autor: Walsh, Ian, Fishman, Dmytro, Garcia-Gasulla, Dario, Titma, Tiina, Pollastri, Gianluca, group, The ELIXIR Machine Learning focus, Harrow, Jen, Psomopoulos, Fotis E., Tosatto, Silvio C. E.

Modern biology frequently relies on machine learning to provide predictions and improve decision processes. There have been recent calls for more scrutiny on machine learning performance and possible limitations. Here we present a set of community-wi

Externí odkaz: http://arxiv.org/abs/2006.16189

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A Survey of End-to-End Driving: Architectures and Training Methods

Autor: Tampuu, Ardi, Semikin, Maksym, Muhammad, Naveed, Fishman, Dmytro, Matiisen, Tambet

Publikováno v: IEEE Transactions on Neural Networks and Learning Systems, 2020

Autonomous driving is of great interest to industry and academia alike. The use of machine learning approaches for autonomous driving has long been studied, but mostly in the context of perception. In this paper we take a deeper look on the so called

Externí odkaz: http://arxiv.org/abs/2003.06404

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