Matthew B. Blaschko

8.0k total citations · 4 hit papers
81 papers, 3.4k citations indexed

About

Matthew B. Blaschko is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Matthew B. Blaschko has authored 81 papers receiving a total of 3.4k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Computer Vision and Pattern Recognition, 40 papers in Artificial Intelligence and 13 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Matthew B. Blaschko's work include Advanced Image and Video Retrieval Techniques (14 papers), Face and Expression Recognition (11 papers) and Domain Adaptation and Few-Shot Learning (11 papers). Matthew B. Blaschko is often cited by papers focused on Advanced Image and Video Retrieval Techniques (14 papers), Face and Expression Recognition (11 papers) and Domain Adaptation and Few-Shot Learning (11 papers). Matthew B. Blaschko collaborates with scholars based in Belgium, Germany and France. Matthew B. Blaschko's co-authors include Christoph H. Lampert, Thomas Hofmann, Maxim Berman, José Ignacio Orlando, Amal Rannen Triki, Elena Prokofyeva, Juho Kannala, Esa Rahtu, Jeroen Bertels and Tom Eelbode and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Access and IEEE Transactions on Biomedical Engineering.

In The Last Decade

Matthew B. Blaschko

75 papers receiving 3.3k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

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

2018 570 citations Maxim Berman, Matthew B. Blaschko et al. Lirias (KU Leuven) profile →
Beyond sliding windows: Object localization by efficient subwindow search

2008 468 citations Matthew B. Blaschko, Thomas Hofmann et al. profile →
A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images

2016 359 citations Matthew B. Blaschko et al. profile →
Optimization for Medical Image Segmentation: Theory and Practice When Evaluating With Dice Score or Jaccard Index

2020 250 citations Tom Eelbode, Jeroen Bertels et al. IEEE Transactions on Medical Imaging profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Matthew B. Blaschko Belgium	22	2.2k	957	710	597	314	81	3.4k
Xin Yang China	30	2.0k 0.9×	1.1k 1.1×	788 1.1×	442 0.7×	519 1.7×	154	3.5k
Mai Xu China	40	3.5k 1.6×	689 0.7×	509 0.7×	284 0.5×	215 0.7×	214	5.6k
Manoranjan Paul Australia	28	1.8k 0.8×	867 0.9×	442 0.6×	380 0.6×	67 0.2×	214	3.0k
Xinge You China	34	2.6k 1.2×	566 0.6×	951 1.3×	256 0.4×	676 2.2×	148	4.2k
Yonghuai Liu United Kingdom	27	1.7k 0.8×	667 0.7×	384 0.5×	343 0.6×	680 2.2×	131	3.3k
Naoufel Werghi United Arab Emirates	28	1.4k 0.6×	517 0.5×	671 0.9×	116 0.2×	190 0.6×	255	2.5k
Karel J. Zuiderveld Netherlands	13	2.1k 1.0×	660 0.7×	285 0.4×	180 0.3×	142 0.5×	37	3.2k
Ismail Ben Ayed Canada	28	1.8k 0.8×	1.0k 1.1×	810 1.1×	66 0.1×	179 0.6×	136	3.1k
Beiji Zou China	24	1.2k 0.5×	802 0.8×	324 0.5×	499 0.8×	43 0.1×	189	2.1k
Muhammad Moazam Fraz Pakistan	27	2.2k 1.0×	2.7k 2.8×	600 0.8×	1.9k 3.1×	53 0.2×	136	3.8k

Countries citing papers authored by Matthew B. Blaschko

Since Specialization

Citations

This map shows the geographic impact of Matthew B. Blaschko's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Matthew B. Blaschko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew B. Blaschko more than expected).

Fields of papers citing papers by Matthew B. Blaschko

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Matthew B. Blaschko. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Matthew B. Blaschko. The network helps show where Matthew B. Blaschko may publish in the future.

Co-authorship network of co-authors of Matthew B. Blaschko

This figure shows the co-authorship network connecting the top 25 collaborators of Matthew B. Blaschko. A scholar is included among the top collaborators of Matthew B. Blaschko based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Matthew B. Blaschko. Matthew B. Blaschko is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Blaschko, Matthew B., et al.. (2024). Temporal Transferability of Tree Species Classification in Temperate Forests with Sentinel-2 Time Series. Remote Sensing. 16(14). 2653–2653. 4 indexed citations

Blaschko, Matthew B., et al.. (2024). A Generic Method for Fine-grained Category Discovery in Natural Language Texts. Lirias (KU Leuven). 3548–3566.

Elen, Bart, Alexander K. Schuster, Matthew B. Blaschko, et al.. (2023). A generalizable deep learning regression model for automated glaucoma screening from fundus images. npj Digital Medicine. 6(1). 112–112. 39 indexed citations

Blaschko, Matthew B., et al.. (2022). Talk2Car: Predicting Physical Trajectories for Natural Language Commands. IEEE Access. 10. 123809–123834. 5 indexed citations

Chakravarty, Punarjay, et al.. (2021). Kinematics Design of a MacPherson Suspension Architecture Based on Bayesian Optimization. IEEE Transactions on Cybernetics. 53(4). 2261–2274. 6 indexed citations

Blaschko, Matthew B., et al.. (2020). Additive Tree-Structured Conditional Parameter Spaces in Bayesian Optimization: A Novel Covariance Function and a Fast Implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 43(9). 3024–3036. 4 indexed citations

Saarakkala, Simo, et al.. (2020). Semixup: In- and Out-of-Manifold Regularization for Deep Semi-Supervised Knee Osteoarthritis Severity Grading From Plain Radiographs. IEEE Transactions on Medical Imaging. 39(12). 4346–4356. 37 indexed citations

Blaschko, Matthew B., et al.. (2020). Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization. Lirias (KU Leuven). 1015–1025. 2 indexed citations

Eelbode, Tom, Jeroen Bertels, Maxim Berman, et al.. (2020). Optimization for Medical Image Segmentation: Theory and Practice When Evaluating With Dice Score or Jaccard Index. IEEE Transactions on Medical Imaging. 39(11). 3679–3690. 250 indexed citations breakdown →

10.

Elen, Bart, João Barbosa‐Breda, Sophie Lemmens, et al.. (2019). Accurate prediction of glaucoma from colour fundus images with a convolutional neural network that relies on active and transfer learning. Acta Ophthalmologica. 98(1). e94–e100. 78 indexed citations

11.

Mehrkanoon, Siamak, Matthew B. Blaschko, & Johan A. K. Suykens. (2018). Shallow and Deep Models for Domain Adaptation problems.. Lirias (KU Leuven). 4 indexed citations

12.

Yu, Jiaqian & Matthew B. Blaschko. (2018). The Lovász Hinge: A Novel Convex Surrogate for Submodular Losses. IEEE Transactions on Pattern Analysis and Machine Intelligence. 42(3). 735–748. 14 indexed citations

13.

Oyallon, Edouard, Sergey Zagoruyko, Nikos Komodakis, et al.. (2018). Scattering Networks for Hybrid Representation Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. 41(9). 2208–2221. 40 indexed citations

14.

Mittal, Arpit, Matthew B. Blaschko, Andrew Zisserman, & Philip H. S. Torr. (2012). Taxonomic Multi-class Prediction and Person Layout Using Efficient Structured Ranking. Lecture notes in computer science. 7573. 245–258. 7 indexed citations

15.

Blaschko, Matthew B., et al.. (2010). Similarities in resting state and feature-driven activity: Non-parametric evaluation of human fMRI. MPG.PuRe (Max Planck Society). 1–2. 1 indexed citations

16.

Blaschko, Matthew B., Andrea Vedaldi, & Andrew Zisserman. (2010). Simultaneous Object Detection and Ranking with Weak Supervision. Oxford University Research Archive (ORA) (University of Oxford). 23. 235–243. 36 indexed citations

17.

Bartels, Andreas, et al.. (2009). Augmenting Feature-driven fMRI Analyses: Semi-supervised learning and resting state activity. Neural Information Processing Systems. 22. 126–134. 4 indexed citations

18.

Blaschko, Matthew B., et al.. (2009). Semi-supervised Subspace Analysis of Human Functional Magnetic Resonance Imaging Data. MPG.PuRe (Max Planck Society). 26(5-6). 629–32. 2 indexed citations

19.

Blaschko, Matthew B. & Arthur Gretton. (2008). A Hilbert-Schmidt Dependence Maximization Approach to Unsupervised Structure Discovery. Max Planck Institute for Plasma Physics. 9(9). 1–3. 1 indexed citations

20.

Blaschko, Matthew B. & Thomas Hofmann. (2006). Conformal Multi-Instance Kernels. Max Planck Institute for Plasma Physics. 1–6. 5 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact