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Matthew B. Blaschko

Matthew B. Blaschko

D-Index & Metrics

Computer Science

D-Index
38
Citations
10341
World Ranking
9994
National Ranking
96

Overview

Matthew B. Blaschko is affiliated with KU Leuven in Belgium and has contributed extensively to the fields of computer science and medicine. Their research predominantly intersects artificial intelligence, computer vision and pattern recognition, radiology and imaging, ophthalmology, and biomedical engineering.

The scientist's recent scholarly works include the following publications:

  • Optimization for Medical Image Segmentation: Theory and Practice When Evaluating With Dice Score or Jaccard Index, 2020, IEEE Transactions on Medical Imaging
  • Metrics reloaded: recommendations for image analysis validation, 2024, Nature Methods
  • Metrics reloaded: Recommendations for image analysis validation, 2022, arXiv (Cornell University)
  • A generalizable deep learning regression model for automated glaucoma screening from fundus images, 2023, npj Digital Medicine
  • Semixup: In- and Out-of-Manifold Regularization for Deep Semi-Supervised Knee Osteoarthritis Severity Grading From Plain Radiographs, 2020, IEEE Transactions on Medical Imaging

Key topics addressed in their research cover:

  • Advanced Neural Network Applications
  • Machine Learning and Data Classification
  • Radiomics and Machine Learning in Medical Imaging
  • Retinal Imaging and Analysis
  • Domain Adaptation and Few-Shot Learning
  • Adversarial Robustness in Machine Learning
  • Glaucoma and Retinal Disorders

Frequent collaborators associated with Matthew B. Blaschko include:

  • Aleksei Tiulpin
  • Marie-Francine Moens
  • Teodora Popordanoska
  • Dusan Grujicic
  • Tinne Tuytelaars

The scientist regularly publishes in venues such as:

  • arXiv (Cornell University)
  • Alzheimer's & Dementia
  • IEEE Transactions on Medical Imaging
  • IEEE Access
  • 2022 26th International Conference on Pattern Recognition (ICPR)

Matthew B. Blaschko's work involves leveraging advanced artificial intelligence methods to improve medical image analysis, emphasizing segmentation, validation metrics, and diagnostic screening. The integration of machine learning techniques into radiology and ophthalmology applications appears as a central theme throughout their body of work.

Best Publications

  • Fine-Grained Visual Classification of Aircraft

    Subhransu Maji;Esa Rahtu;Juho Kannala;Matthew B. Blaschko

  • Beyond sliding windows: Object localization by efficient subwindow search

    C.H. Lampert;M.B. Blaschko;T. Hofmann

  • The Lovasz-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks

    Maxim Berman;Amal Rannen Triki;Matthew B. Blaschko

  • A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images

    Jose Ignacio Orlando;Elena Prokofyeva;Matthew B. Blaschko

  • Efficient Subwindow Search: A Branch and Bound Framework for Object Localization

    C.H. Lampert;M.B. Blaschko;T. Hofmann

  • Learning to Localize Objects with Structured Output Regression

    Matthew Blaschko;Christoph H Lampert

  • Optimization for Medical Image Segmentation: Theory and Practice When Evaluating With Dice Score or Jaccard Index

    Tom Eelbode;Jeroen Bertels;Maxim Berman;Dirk Vandermeulen

  • Encoder Based Lifelong Learning

    Amal Rannen;Rahaf Aljundi;Matthew B. Blaschko;Tinne Tuytelaars

  • An ensemble deep learning based approach for red lesion detection in fundus images.

    José Ignacio Orlando;Elena Prokofyeva;Mirta Mariana del Fresno;Matthew Brian Blaschko

  • Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory and Practice

    Jeroen Bertels;Tom Eelbode;Maxim Berman;Dirk Vandermeulen

  • Combining Local and Global Image Features for Object Class Recognition

    D.A. Lisin;M.A. Mattar;M.B. Blaschko;E.G. Learned-Miller

  • Correlational spectral clustering

    M.B. Blaschko;C.H. Lampert

  • Unsupervised Object Discovery: A Comparison

    Tinne Tuytelaars;Christoph H. Lampert;Matthew B. Blaschko;Wray Buntine

  • Learning a category independent object detection cascade

    Esa Rahtu;Juho Kannala;Matthew Blaschko

  • Encoder Based Lifelong Learning

    Amal Rannen Triki;Rahaf Aljundi;Mathew B. Blaschko;Tinne Tuytelaars

  • Deep learning on fundus images detects glaucoma beyond the optic disc.

    Ruben Hemelings;Ruben Hemelings;Bart Elen;João Barbosa-Breda;João Barbosa-Breda;Matthew B. Blaschko

  • Convolutional neural network transfer for automated glaucoma identification

    José Ignacio Orlando;Elena Prokofyeva;Mariana del Fresno;Matthew B. Blaschko

  • Learning Fully-Connected CRFs for Blood Vessel Segmentation in Retinal Images

    José Ignacio Orlando;Matthew B. Blaschko

  • Understanding Objects in Detail with Fine-Grained Attributes

    Andrea Vedaldi;Siddharth Mahendran;Stavros Tsogkas;Subhransu Maji

  • Automatic In Situ Identification of Plankton

    M.B. Blaschko;G. Holness;M.A. Mattar;D. Lisin

  • Artery-vein segmentation in fundus images using a fully convolutional network.

    Ruben Hemelings;Bart Elen;Ingeborg Stalmans;Karel Van Keer

  • The Lov'asz-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks

    Maxim Berman;Amal Rannen Triki;Matthew B. Blaschko

Frequent Co-Authors

Arthur Gretton
Arthur Gretton University College London
Christoph H. Lampert
Christoph H. Lampert Institute of Science and Technology Austria
Esa Rahtu
Esa Rahtu Tampere University
Gaël Varoquaux
Gaël Varoquaux French Institute for Research in Computer Science and Automation - INRIA
Juho Kannala
Juho Kannala Aalto University
Andreas Bartels
Andreas Bartels Max Planck Society
Andrea Vedaldi
Andrea Vedaldi University of Oxford
Frederik Maes
Frederik Maes KU Leuven

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