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2020 – today
- 2024
[i6]Irina Zhang, Jim Denholm, Azam Hamidinekoo, Oskar Ålund, Christopher Bagnall, Joana Palés Huix, Michal Sulikowski, Ortensia Vito, Arthur Lewis, Robert Unwin, Magnus Söderberg, Nikolay Burlutskiy, Talha Qaiser:
Utilizing Weak-to-Strong Consistency for Semi-Supervised Glomeruli Segmentation. CoRR abs/2406.16900 (2024)- 2023
- [c8]Ricardo Mokhtari, Azam Hamidinekoo, Daniel James Sutton, Arthur Lewis, Bastian Angermann, Ulf Gehrmann, Pål Lundin, Hibret Adissu, Junmei Cairns, Jessica Neisen, Emon Khan, Daniel Marks, Nia Khachapuridze, Talha Qaiser, Nikolay Burlutskiy:
Interpretable histopathology-based prediction of disease relevant features in Inflammatory Bowel Disease biopsies using weakly-supervised deep learning. MIDL 2023: 479-495 - [i5]Ricardo Mokhtari, Azam Hamidinekoo, Daniel James Sutton, Arthur Lewis, Bastian Angermann, Ulf Gehrmann, Pål Lundin, Hibret Adissu, Junmei Cairns, Jessica Neisen, Emon Khan, Daniel Marks, Nia Khachapuridze, Talha Qaiser, Nikolay Burlutskiy:
Interpretable histopathology-based prediction of disease relevant features in Inflammatory Bowel Disease biopsies using weakly-supervised deep learning. CoRR abs/2303.12095 (2023) - 2022
- [c7]Sophie Peacock, Etai Jacob, Nikolay Burlutskiy:
Coupling Deep Imputation with Multitask Learning for Downstream Tasks on Omics Data. IJCNN 2022: 1-10 - [i4]Sophie Peacock, Etai Jacob, Nikolay Burlutskiy:
Coupling Deep Imputation with Multitask Learning for Downstream Tasks on Genomics Data. CoRR abs/2204.13705 (2022) - 2021
- [e1]Manfredo Atzori, Nikolay Burlutskiy, Francesco Ciompi, Zhang Li, Fayyaz A. Minhas, Henning Müller, Tingying Peng, Nasir M. Rajpoot, Ben Torben-Nielsen, Jeroen van der Laak, Mitko Veta, Yinyin Yuan, Inti Zlobec:
MICCAI Workshop on Computational Pathology, COMPAY@MICCAI 2021, 27 September 2021, Virtual Event. Proceedings of Machine Learning Research 156, PMLR 2021 [contents]
2010 – 2019
- 2019
- [c6]Nikolay Burlutskiy, Nicolas Pinchaud, Feng Gu, Daniel Hägg, Mats Andersson, Lars Björk, Kristian Eurén, Cristina Svensson, Lena Kajland Wilén, Martin Hedlund:
Segmenting Potentially Cancerous Areas in Prostate Biopsies using Semi-Automatically Annotated Data. MIDL 2019: 92-108 - [i3]Nikolay Burlutskiy, Nicolas Pinchaud, Feng Gu, Daniel Hägg, Mats Andersson, Lars Björk, Kristian Eurén, Cristina Svensson, Lena Kajland Wilén, Martin Hedlund:
Segmenting Potentially Cancerous Areas in Prostate Biopsies using Semi-Automatically Annotated Data. CoRR abs/1904.06969 (2019) - 2018
- [c5]Feng Gu, Nikolay Burlutskiy, Mats Andersson, Lena Kajland Wilén:
Multi-resolution Networks for Semantic Segmentation in Whole Slide Images. COMPAY/OMIA@MICCAI 2018: 11-18 - [i2]Feng Gu, Nikolay Burlutskiy, Mats Andersson, Lena Kajland Wilen:
Multi-Resolution Networks for Semantic Segmentation in Whole Slide Images. CoRR abs/1807.09607 (2018) - [i1]Nikolay Burlutskiy, Feng Gu, Lena Kajland Wilen, Max Backman, Patrick Micke:
A Deep Learning Framework for Automatic Diagnosis in Lung Cancer. CoRR abs/1807.10466 (2018) - 2017
- [b1]Nikolay Burlutskiy:
Prediction of user behaviour on the Web. University of Brighton, Eastbourne, Hastings, UK, 2017 - 2016
- [c4]Nikolay Burlutskiy, Miltos Petridis, Andrew Fish
, Alexey Chernov, Nour Ali:
An Investigation on Online Versus Batch Learning in Predicting User Behaviour. SGAI Conf. 2016: 135-149 - 2015
- [c3]Mithileysh Sathiyanarayanan, Nikolay Burlutskiy:
Design and evaluation of euler diagram and treemap for social network visualisation. COMSNETS 2015: 1-6 - [c2]Nikolay Burlutskiy, Andrew Fish, Nour Ali, Miltos Petridis:
Prediction of Users' Response Time in Q&A Communities. ICMLA 2015: 618-623 - 2014
- [c1]Nikolay Burlutskiy, Miltos Petridis, Andrew Fish, Nour Ali:
Enabling the visualization for reasoning about temporal data. VL/HCC 2014: 179-180
Coauthor Index
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