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

3 code implementations • 25 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

2018 Robotic Scene Segmentation Challenge

3 code implementations • 30 Jan 2020 • Max Allan, Satoshi Kondo, Sebastian Bodenstedt, Stefan Leger, Rahim Kadkhodamohammadi, Imanol Luengo, Felix Fuentes, Evangello Flouty, Ahmed Mohammed, Marius Pedersen, Avinash Kori, Varghese Alex, Ganapathy Krishnamurthi, David Rauber, Robert Mendel, Christoph Palm, Sophia Bano, Guinther Saibro, Chi-Sheng Shih, Hsun-An Chiang, Juntang Zhuang, Junlin Yang, Vladimir Iglovikov, Anton Dobrenkii, Madhu Reddiboina, Anubhav Reddy, Xingtong Liu, Cong Gao, Mathias Unberath, Myeonghyeon Kim, Chanho Kim, Chaewon Kim, Hye-Jin Kim, Gyeongmin Lee, Ihsan Ullah, Miguel Luna, Sang Hyun Park, Mahdi Azizian, Danail Stoyanov, Lena Maier-Hein, Stefanie Speidel

In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward kinematics and instrument CAD models.

Scene Segmentation Segmentation

2017 Robotic Instrument Segmentation Challenge

3 code implementations • 18 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 implementations • 6 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 implementation • 21 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.

Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning

2 code implementations • 3 Mar 2018 • Alexey Shvets, Alexander Rakhlin, Alexandr A. Kalinin, Vladimir Iglovikov

Semantic segmentation of robotic instruments is an important problem for the robot-assisted surgery.

Pose Estimation Segmentation +1

Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis

3 code implementations • 2 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

TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation

13 code implementations • 17 Jan 2018 • Vladimir Iglovikov, Alexey Shvets

Pixel-wise image segmentation is demanding task in computer vision.

Image Segmentation Segmentation +1

Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks

no code implementations • 13 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.

Satellite Imagery Feature Detection using Deep Convolutional Neural Network: A Kaggle Competition

no code implementations • 19 Jun 2017 • Vladimir Iglovikov, Sergey Mushinskiy, Vladimir Osin

This paper describes our approach to the DSTL Satellite Imagery Feature Detection challenge run by Kaggle.

Data Augmentation Semantic Segmentation