The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Peter Horvath - One of the best experts on this subject based on the ideXlab platform.
-
3d cell Annotator an open source active surface tool for single cell segmentation in 3d microscopy images
Bioinformatics, 2020Co-Authors: Ervin Tasnadi, Timea Toth, Maria Kovacs, Akos Diosdi, Francesco Pampaloni, J Molnar, Filippo Piccinini, Peter HorvathAbstract:SUMMARY Segmentation of single cells in microscopy images is one of the major challenges in computational biology. It is the first step of most bioimage analysis tasks, and essential to create training sets for more advanced deep learning approaches. Here, we propose 3D-Cell-Annotator to solve this task using 3D active surfaces together with shape descriptors as prior information in a semi-automated fashion. The software uses the convenient 3D interface of the widely used Medical Imaging Interaction Toolkit (MITK). Results on 3D biological structures (e.g. spheroids, organoids and embryos) show that the precision of the segmentation reaches the level of a human expert. AVAILABILITY AND IMPLEMENTATION 3D-Cell-Annotator is implemented in CUDA/C++ as a patch for the segmentation module of MITK. The 3D-Cell-Annotator enabled MITK distribution can be downloaded at: www.3D-cell-Annotator.org. It works under Windows 64-bit systems and recent Linux distributions even on a consumer level laptop with a CUDA-enabled video card using recent NVIDIA drivers. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
-
3d cell Annotator an open source active surface tool for single cell segmentation in 3d microscopy images
bioRxiv, 2019Co-Authors: Ervin Tasnadi, Timea Toth, Maria Kovacs, Akos Diosdi, Francesco Pampaloni, J Molnar, Filippo Piccinini, Peter HorvathAbstract:Segmentation of single cells in microscopy images is one of the major challenges in computational biology. It is the first step of most bioimage analysis tasks, and essential to create training sets for more advanced deep learning approaches. Here, we propose 3D-Cell-Annotator to solve this task using 3D active surfaces together with shape descriptors as prior information in a fully- and semi-automated fashion. The software uses the convenient 3D interface of the widely used Medical Imaging Interaction Toolkit (MITK). Results on 3D biological structures (e.g. spheroids, organoids, embryos) show that the precision of the segmentation reaches the level of a human expert. Availability and implementation: 3D-Cell-Annotator is implemented in CUDA/C++ as a patch for the segmentation module of MITK. The 3D-Cell-Annotator enabled MITK distribution can be downloaded at: www.3D-cell-Annotator.org. It works under Windows 64-bit systems and recent Linux distributions even on a consumer level laptop with a CUDA-enabled video card using recent NVIDIA drivers. Contacts: filippo.piccinini@irst.emr.it and horvath.peter@brc.mta.hu
Rafael E Banchs - One of the best experts on this subject based on the ideXlab platform.
-
Using annotations on Mechanical Turk to perform supervised polarity classification of Spanish customer comments
Information Sciences, 2014Co-Authors: Marta R. Costa-jussà, Bart Mellebeek, Francesc Benavent, Jens Grivolla, Joan Codina, Rafael E BanchsAbstract:One of the major bottlenecks in the development of data-driven AI Systems is the cost of reliable human annotations. The recent advent of several crowdsourcing platforms such as Amazon’s Mechanical Turk, allowing requesters the access to affordable and rapid results of a global workforce, greatly facilitates the creation of massive training data. Most of the available studies on the effectiveness of crowdsourcing report on English data. We use Mechanical Turk annotations to train an Opinion Mining System to classify Spanish consumer comments. We design three different Human Intelligence Task (HIT) strategies and report high inter-Annotator agreement between non-experts and expert Annotators. We evaluate the advantages/drawbacks of each HIT design and show that, in our case, the use of non-expert annotations is a viable and cost-effective alternative to expert annotations.
-
opinion mining of spanish customer comments with non expert annotations on mechanical turk
North American Chapter of the Association for Computational Linguistics, 2010Co-Authors: Bart Mellebeek, Francesc Benavent, Jens Grivolla, Joan Codina, Marta R Costajussa, Rafael E BanchsAbstract:One of the major bottlenecks in the development of data-driven AI Systems is the cost of reliable human annotations. The recent advent of several crowdsourcing platforms such as Amazon's Mechanical Turk, allowing requesters the access to affordable and rapid results of a global workforce, greatly facilitates the creation of massive training data. Most of the available studies on the effectiveness of crowdsourcing report on English data. We use Mechanical Turk annotations to train an Opinion Mining System to classify Spanish consumer comments. We design three different Human Intelligence Task (HIT) strategies and report high inter-Annotator agreement between non-experts and expert Annotators. We evaluate the advantages/drawbacks of each HIT design and show that, in our case, the use of non-expert annotations is a viable and cost-effective alternative to expert annotations.
Louis Bosch - One of the best experts on this subject based on the ideXlab platform.
-
A tool for efficient and accurate segmentation of speech data: announcing POnSS
Behavior Research Methods, 2020Co-Authors: Joe Rodd, Caitlin Decuyper, Hans Rutger Bosker, Louis BoschAbstract:Despite advances in automatic speech recognition (ASR), human input is still essential for producing research-grade segmentations of speech data. Conventional approaches to manual segmentation are very labor-intensive. We introduce POnSS, a browser-based system that is specialized for the task of segmenting the onsets and offsets of words, which combines aspects of ASR with limited human input. In developing POnSS, we identified several sub-tasks of segmentation, and implemented each of these as separate interfaces for the Annotators to interact with to streamline their task as much as possible. We evaluated segmentations made with POnSS against a baseline of segmentations of the same data made conventionally in Praat. We observed that POnSS achieved comparable reliability to segmentation using Praat, but required 23% less Annotator time investment. Because of its greater efficiency without sacrificing reliability, POnSS represents a distinct methodological advance for the segmentation of speech data.
Gang Hua - One of the best experts on this subject based on the ideXlab platform.
-
Multi-class Multi-Annotator Active Learning with Robust Gaussian Process for Visual Recognition
2015 IEEE International Conference on Computer Vision (ICCV), 2015Co-Authors: Chengjiang Long, Gang HuaAbstract:Active learning is an effective way to relieve the tedious work of manual annotation in many applications of visual recognition. However, less research attention has been focused on multi-class active learning. In this paper, we propose a novel Gaussian process classifier model with multiple Annotators for multi-class visual recognition. Expectation propagation (EP) is adopted for efficient approximate Bayesian inference of our probabilistic model for classification. Based on the EP approximation inference, a generalized Expectation Maximization (GEM) algorithm is derived to estimate both the parameters for instances and the quality of each individual Annotator. Also, we incorporate the idea of reinforcement learning to actively select both the informative samples and the high-quality Annotators, which better explores the trade-off between exploitation and exploration. The experiments clearly demonstrate the efficacy of the proposed model.
Bart Mellebeek - One of the best experts on this subject based on the ideXlab platform.
-
Using annotations on Mechanical Turk to perform supervised polarity classification of Spanish customer comments
Information Sciences, 2014Co-Authors: Marta R. Costa-jussà, Bart Mellebeek, Francesc Benavent, Jens Grivolla, Joan Codina, Rafael E BanchsAbstract:One of the major bottlenecks in the development of data-driven AI Systems is the cost of reliable human annotations. The recent advent of several crowdsourcing platforms such as Amazon’s Mechanical Turk, allowing requesters the access to affordable and rapid results of a global workforce, greatly facilitates the creation of massive training data. Most of the available studies on the effectiveness of crowdsourcing report on English data. We use Mechanical Turk annotations to train an Opinion Mining System to classify Spanish consumer comments. We design three different Human Intelligence Task (HIT) strategies and report high inter-Annotator agreement between non-experts and expert Annotators. We evaluate the advantages/drawbacks of each HIT design and show that, in our case, the use of non-expert annotations is a viable and cost-effective alternative to expert annotations.
-
opinion mining of spanish customer comments with non expert annotations on mechanical turk
North American Chapter of the Association for Computational Linguistics, 2010Co-Authors: Bart Mellebeek, Francesc Benavent, Jens Grivolla, Joan Codina, Marta R Costajussa, Rafael E BanchsAbstract:One of the major bottlenecks in the development of data-driven AI Systems is the cost of reliable human annotations. The recent advent of several crowdsourcing platforms such as Amazon's Mechanical Turk, allowing requesters the access to affordable and rapid results of a global workforce, greatly facilitates the creation of massive training data. Most of the available studies on the effectiveness of crowdsourcing report on English data. We use Mechanical Turk annotations to train an Opinion Mining System to classify Spanish consumer comments. We design three different Human Intelligence Task (HIT) strategies and report high inter-Annotator agreement between non-experts and expert Annotators. We evaluate the advantages/drawbacks of each HIT design and show that, in our case, the use of non-expert annotations is a viable and cost-effective alternative to expert annotations.
