2.3k citations · 4.2k across the 16 of their papers we have counts for
18 papers
RGB Input Pipelines: Throughput, GPU Memory, and Transformation Coverage
Vladimir Iglovikov
An image-augmentation pipeline must deliver a complete batch before a model can use it. We compare seven input paths from five libraries, starting with RGB JPEG files and ending wi…
AlbumentationsX: One Augmentation Pipeline for Images and Related Annotations
Vladimir Iglovikov
Augmentation can corrupt a training example when an image and its annotations receive different random changes. A crop must use the same coordinates for the image, mask, boxes, key…
One Thousand and One Hours: Self-driving Motion Prediction Dataset
John Houston, Guido Zuidhof, Luca Bergamini +6
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. This w…
2018 Robotic Scene Segmentation Challenge
Max Allan, Satoshi Kondo, Sebastian Bodenstedt +38
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 ki…
Breast Tumor Cellularity Assessment using Deep Neural Networks
Alexander Rakhlin, Aleksei Tiulpin, Alexey A. Shvets +3
Breast cancer is one of the main causes of death worldwide. Histopathological cellularity assessment of residual tumors in post-surgical tissues is used to analyze a tumor's respon…
2017 Robotic Instrument Segmentation Challenge
Max Allan, Alex Shvets, Thomas Kurmann +16
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 t…
Camera Model Identification Using Convolutional Neural Networks
Artur Kuzin, Artur Fattakhov, Ilya Kibardin +2
Source camera identification is the process of determining which camera or model has been used to capture an image. In the recent years, there has been a rapid growth of research i…
Albumentations: fast and flexible image augmentations
Alexander Buslaev, Alex Parinov, Eugene Khvedchenya +2
Data augmentation is a commonly used technique for increasing both the size and the diversity of labeled training sets by leveraging input transformations that preserve output labe…
Fully Convolutional Network for Automatic Road Extraction from Satellite Imagery
Alexander V. Buslaev, Selim S. Seferbekov, Vladimir I. Iglovikov +1
Analysis of high-resolution satellite images has been an important research topic for traffic management, city planning, and road monitoring. One of the problems here is automatic…
Feature Pyramid Network for Multi-Class Land Segmentation
Selim S. Seferbekov, Vladimir I. Iglovikov, Alexander V. Buslaev +1
Semantic segmentation is in-demand in satellite imagery processing. Because of the complex environment, automatic categorization and segmentation of land cover is a challenging pro…
TernausNetV2: Fully Convolutional Network for Instance Segmentation
Vladimir I. Iglovikov, Selim Seferbekov, Alexander V. Buslaev +1
The most common approaches to instance segmentation are complex and use two-stage networks with object proposals, conditional random-fields, template matching or recurrent neural n…
Angiodysplasia Detection and Localization Using Deep Convolutional Neural Networks
Alexey Shvets, Vladimir Iglovikov, Alexander Rakhlin +1
Accurate detection and localization for angiodysplasia lesions is an important problem in early stage diagnostics of gastrointestinal bleeding and anemia. Gold-standard for angiody…
Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis
Alexander Rakhlin, Alexey Shvets, Vladimir Iglovikov +1
Breast cancer is one of the main causes of cancer death worldwide. Early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedi…
Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning
Alexey Shvets, Alexander Rakhlin, Alexandr A. Kalinin +1
Semantic segmentation of robotic instruments is an important problem for the robot-assisted surgery. One of the main challenges is to correctly detect an instrument's position for…
TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation
Vladimir Iglovikov, Alexey Shvets
Pixel-wise image segmentation is demanding task in computer vision. Classical U-Net architectures composed of encoders and decoders are very popular for segmentation of medical ima…
Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks
Vladimir Iglovikov, Alexander Rakhlin, Alexandr Kalinin +1
Skeletal bone age assessment is a common clinical practice to diagnose endocrine and metabolic disorders in child development. In this paper, we describe a fully automated deep lea…
Satellite Imagery Feature Detection using Deep Convolutional Neural Network: A Kaggle Competition
Vladimir Iglovikov, Sergey Mushinskiy, Vladimir Osin
This paper describes our approach to the DSTL Satellite Imagery Feature Detection challenge run by Kaggle. The primary goal of this challenge is accurate semantic segmentation of d…
Disorder Line and Incommensurate Floating Phases in the Quantum Ising Model on an Anisotropic Triangular Lattice
V. I. Iglovikov, R. T. Scalettar, J. Oitmaa +1
We present a Quantum Monte Carlo study of the Ising model in a transverse field on a square lattice with nearest-neighbor antiferromagnetic exchange interaction J and one diagonal…