Ender Konukoglu

Bio: Ender Konukoglu is an academic researcher from ETH Zurich. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 43, co-authored 182 publications receiving 9747 citations. Previous affiliations of Ender Konukoglu include Beijing Institute of Technology & French Institute for Research in Computer Science and Automation.

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Abstract: In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients—manually annotated by up to four raters—and to 65 comparable scans generated using tumor image simulation software Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%–85%), illustrating the difficulty of this task We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource

Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold, Learning and Semi-supervised Learning

Abstract: This review presents a unified, efficient model of random decision forests which can be applied to a number of machine learning, computer vision, and medical image analysis tasks. Our model extends existing forest-based techniques as it unifies classification, regression, density estimation, manifold learning, semi-supervised learning, and active learning under the same decision forest framework. This gives us the opportunity to write and optimize the core implementation only once, with application to many diverse tasks. The proposed model may be used both in a discriminative or generative way and may be applied to discrete or continuous, labeled or unlabeled data. The main contributions of this review are: (1) Proposing a unified, probabilistic and efficient model for a variety of learning tasks; (2) Demonstrating margin-maximizing properties of classification forests; (3) Discussing probabilistic regression forests in comparison with other nonlinear regression algorithms; (4) Introducing density forests for estimating probability density functions; (5) Proposing an efficient algorithm for sampling from a density forest; (6) Introducing manifold forests for nonlinear dimensionality reduction; (7) Proposing new algorithms for transductive learning and active learning. Finally, we discuss how alternatives such as random ferns and extremely randomized trees stem from our more general forest model. This document is directed at both students who wish to learn the basics of decision forests, as well as researchers interested in the new contributions. It presents both fundamental and novel concepts in a structured way, with many illustrative examples and real-world applications. Thorough comparisons with state-of-the-art algorithms such as support vector machines, boosting and Gaussian processes are presented and relative advantages and disadvantages discussed. The many synthetic examples and existing commercial applications demonstrate the validity of the proposed model and its flexibility.

Regression forests for efficient anatomy detection and localization in CT studies

Abstract: This paper proposes multi-class random regression forests as an algorithm for the efficient, automatic detection and localization of anatomical structures within three-dimensional CT scans Regression forests are similar to the more popular classification forests, but trained to predict continuous outputs We introduce a new, continuous parametrization of the anatomy localization task which is effectively addressed by regression forests This is shown to be a more natural approach than classification A single pass of our probabilistic algorithm enables the direct mapping from voxels to organ location and size; with training focusing on maximizing the confidence of output predictions As a by-product, our method produces salient anatomical landmarks; ie automatically selected "anchor" regions which help localize organs of interest with high confidence Quantitative validation is performed on a database of 100 highly variable CT scans Localization errors are shown to be lower (and more stable) than those from global affine registration approaches The regressor's parallelism and the simplicity of its context-rich visual features yield typical runtimes of only 1s Applications include semantic visual navigation, image tagging for retrieval, and initializing organ-specific processing

Shape-based hand recognition

Abstract: The problem of person recognition and verification based on their hand images has been addressed. The system is based on the images of the right hands of the subjects, captured by a flatbed scanner in an unconstrained pose at 45 dpi. In a preprocessing stage of the algorithm, the silhouettes of hand images are registered to a fixed pose, which involves both rotation and translation of the hand and, separately, of the individual fingers. Two feature sets have been comparatively assessed, Hausdorff distance of the hand contours and independent component features of the hand silhouette images. Both the classification and the verification performances are found to be very satisfactory as it was shown that, at least for groups of about five hundred subjects, hand-based recognition is a viable secure access control scheme.

Decision Forests for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning

Abstract: This paper presents a unified, efficient model of random decision forests which can be applied to a number of machine learning, computer vision and medical image analysis tasks Our model extends existing forest-based techniques as it unifies classification, regression, density estimation, manifold learning, semi-supervised learning and active learning under the same decision forest framework This means that the core implementation needs be written and optimized only once, and can then be applied to many diverse tasks The proposed model may be used both in a generative or discriminative way and may be applied to discrete or continuous, labelled or unlabelled data The main contributions of this paper are: 1) proposing a single, probabilistic and efficient model for a variety of learning tasks; 2) demonstrating margin-maximizing properties of classification forests; 3) introducing density forests for learning accurate probability density functions; 4) proposing efficient algorithms for sampling from the forest generative model; 5) introducing manifold forests for non-linear embedding and dimensionality reduction; 6) proposing new and efficient forest-based algorithms for transductive and active learning We discuss how alternatives such as random ferns and extremely randomized trees stem from our more general model This paper is directed at both students who wish to learn the basics of decision forests, as well as researchers interested in our new contributions It presents both fundamental and novel concepts in a structured way, with many illustrative examples and real-world applications Thorough comparisons with state of the art algorithms such as support vector machines, boosting and Gaussian processes are presented and relative advantages and disadvantages discussedThe many synthetic examples and existing commercial applications demonstrate the validity of the proposed model and its flexibility Powerpoint slides (with many examples and animations) are also available from http://researchmicrosoftcom/groups/vision/decisionforestsaspx

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

Abstract: Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks. In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computer-aided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks.

Taking the Human Out of the Loop: A Review of Bayesian Optimization

Abstract: Big Data applications are typically associated with systems involving large numbers of users, massive complex software systems, and large-scale heterogeneous computing and storage architectures. The construction of such systems involves many distributed design choices. The end products (e.g., recommendation systems, medical analysis tools, real-time game engines, speech recognizers) thus involve many tunable configuration parameters. These parameters are often specified and hard-coded into the software by various developers or teams. If optimized jointly, these parameters can result in significant improvements. Bayesian optimization is a powerful tool for the joint optimization of design choices that is gaining great popularity in recent years. It promises greater automation so as to increase both product quality and human productivity. This review paper introduces Bayesian optimization, highlights some of its methodological aspects, and showcases a wide range of applications.

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Abstract: In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients—manually annotated by up to four raters—and to 65 comparable scans generated using tumor image simulation software Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%–85%), illustrating the difficulty of this task We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource