Search Results for author: Vladimir Iglovikov

Found 10 papers, 7 papers with code

One Thousand and One Hours: Self-driving Motion Prediction Dataset

3 code implementations25 Jun 2020 John Houston, Guido Zuidhof, Luca Bergamini, Yawei Ye, Long Chen, Ashesh Jain, Sammy Omari, Vladimir Iglovikov, Peter Ondruska

Motivated by the impact of large-scale datasets on ML systems we present the largest self-driving dataset for motion prediction to date, containing over 1, 000 hours of data.

Autonomous Vehicles Motion Forecasting +2

2017 Robotic Instrument Segmentation Challenge

3 code implementations18 Feb 2019 Max Allan, Alex Shvets, Thomas Kurmann, Zichen Zhang, Rahul Duggal, Yun-Hsuan Su, Nicola Rieke, Iro Laina, Niveditha Kalavakonda, Sebastian Bodenstedt, Luis Herrera, Wenqi Li, Vladimir Iglovikov, Huoling Luo, Jian Yang, Danail Stoyanov, Lena Maier-Hein, Stefanie Speidel, Mahdi Azizian

In mainstream computer vision and machine learning, public datasets such as ImageNet, COCO and KITTI have helped drive enormous improvements by enabling researchers to understand the strengths and limitations of different algorithms via performance comparison.

Benchmarking Person Re-Identification +2

Camera Model Identification Using Convolutional Neural Networks

no code implementations6 Oct 2018 Artur Kuzin, Artur Fattakhov, Ilya Kibardin, Vladimir Iglovikov, Ruslan Dautov

Source camera identification is the process of determining which camera or model has been used to capture an image.

Angiodysplasia Detection and Localization Using Deep Convolutional Neural Networks

1 code implementation21 Apr 2018 Alexey Shvets, Vladimir Iglovikov, Alexander Rakhlin, Alexandr A. Kalinin

Accurate detection and localization for angiodysplasia lesions is an important problem in early stage diagnostics of gastrointestinal bleeding and anemia.

Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis

3 code implementations2 Feb 2018 Alexander Rakhlin, Alexey Shvets, Vladimir Iglovikov, Alexandr A. Kalinin

In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image classification.

Breast Cancer Detection Classification +4

Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks

no code implementations13 Dec 2017 Vladimir Iglovikov, Alexander Rakhlin, Alexandr Kalinin, Alexey Shvets

Skeletal bone age assessment is a common clinical practice to diagnose endocrine and metabolic disorders in child development.

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