Leo Grady

7.7k total citations · 1 hit paper
75 papers, 4.4k citations indexed

About

Leo Grady is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, Leo Grady has authored 75 papers receiving a total of 4.4k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Computer Vision and Pattern Recognition, 31 papers in Radiology, Nuclear Medicine and Imaging and 12 papers in Artificial Intelligence. Recurrent topics in Leo Grady's work include Medical Image Segmentation Techniques (33 papers), Advanced Neural Network Applications (12 papers) and Medical Imaging Techniques and Applications (11 papers). Leo Grady is often cited by papers focused on Medical Image Segmentation Techniques (33 papers), Advanced Neural Network Applications (12 papers) and Medical Imaging Techniques and Applications (11 papers). Leo Grady collaborates with scholars based in United States, Germany and France. Leo Grady's co-authors include Jon̈athan R. Polimeni, Ali Kemal Sinop, Eli Schwartz, Hervé Lombaert, Laurent Najman, Hugues Talbot, Camille Couprie, Marie‐Pierre Jolly, Eric L. Schwartz and Chenyang Xu and has published in prestigious journals such as Journal of Clinical Oncology, Journal of the American College of Cardiology and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

Leo Grady

73 papers receiving 4.2k citations

Hit Papers

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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.

Random Walks for Image Segmentation

2006 1.7k citations Leo Grady IEEE Transactions on Pattern Analysis and Machine Intelligence profile →

Peers

Countries citing papers authored by Leo Grady

Since Specialization

Citations

This map shows the geographic impact of Leo Grady'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 Leo Grady with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Leo Grady more than expected).

Fields of papers citing papers by Leo Grady

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Leo Grady. 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 Leo Grady. The network helps show where Leo Grady may publish in the future.

Co-authorship network of co-authors of Leo Grady

This figure shows the co-authorship network connecting the top 25 collaborators of Leo Grady. A scholar is included among the top collaborators of Leo Grady 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 Leo Grady. Leo Grady is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies 2020 ·Modern Pathology ·Patricia Raciti, Jillian Sue, (unknown), Ran Godrich, Jeremy D. Kunz, (unknown), Victor E. Reuter, Leo Grady, Christopher Kanan, David S. Klimstra, Thomas J. Fuchs	121
2	Automated estimation of image quality for coronary computed tomographic angiography using machine learning 2018 ·European Radiology ·Rine Nakanishi, Sethuraman Sankaran, Leo Grady, (unknown), (unknown), Kazuhiro Osawa, Indrė Čeponienė, (unknown), (unknown), (unknown), (unknown), Christopher Dailing, Christopher K. Zarins, Bon‐Kwon Koo, James K. Min, Charles A. Taylor, Matthew J. Budoff	19
3	Fast geometric sensitivity analysis in hemodynamic simulations using a machine learning approach 2013 ·Bulletin of the American Physical Society ·Sethuraman Sankaran, Leo Grady, Charles Taylor	1
4	Learning the Manifold of Quality Ultrasound Acquisition 2013 ·Lecture notes in computer science ·(unknown), Michelle Yan, Sara V. Good, Tong Fang, S. Kevin Zhou, Leo Grady	5
5	Global error minimization in image mosaicing using graph connectivity and its applications in microscopy 2012 ·Journal of Pathology Informatics ·Parmeshwar Khurd, Leo Grady, Rafiou Oketokoun, Hari Sundar, (unknown), Summer L. Gibbs‐Strauss, John V. Frangioni, Ali Kamen	7
6	Random walks based multi-image segmentation: Quasiconvexity results and GPU-based solutions 2012 ·PubMed ·Marcus D. Collins, Jia Xu, Leo Grady, Vijendra Singh	72
7	Network, Anatomical, and Non-Imaging Measures for the Prediction of ADHD Diagnosis in Individual Subjects 2012 ·Frontiers in Systems Neuroscience ·Jason W. Bohland, (unknown), Francisco Pereira, Jérémy Rapin, Leo Grady	52
8	Evaluating Segmentation Error without Ground Truth 2012 ·Lecture notes in computer science ·Timo Kohlberger, Vivek Kumar Singh, (unknown), Claus Bahlmann, Leo Grady	48
9	Automated malignancy detection in breast histopathological images 2012 ·Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE ·Andrei Chekkoury, Parmeshwar Khurd, Jie Ni, Claus Bahlmann, Ali Kamen, Amar Patel, Leo Grady, Maneesh Singh, Martin Groher, Nassir Navab, Elizabeth A. Krupinski, Jeffrey Johnson, Anna R. Graham, Ronald S. Weinstein	33
10	Denoising sparse images from GRAPPA using the nullspace method (DESIGN) 2011 ·DSpace@MIT (Massachusetts Institute of Technology) ·Daniel S. Weller, Jon̈athan R. Polimeni, Leo Grady, Lawrence L. Wald, Elfar Adalsteinsson, Vivek K Goyal	2
11	Denoising sparse images from GRAPPA using the nullspace method 2011 ·Magnetic Resonance in Medicine ·Daniel S. Weller, Jon̈athan R. Polimeni, Leo Grady, Lawrence L. Wald, Elfar Adalsteinsson, Vivek K Goyal	19
12	Fast Brain Matching with Spectral Correspondence 2011 ·Lecture notes in computer science ·Hervé Lombaert, Leo Grady, Jon̈athan R. Polimeni, Farida Chériet	21
13	The Piecewise Smooth Mumford–Shah Functional on an Arbitrary Graph 2009 ·IEEE Transactions on Image Processing ·Leo Grady, Christopher Alvino	42
14	Minimal Surfaces Extend Shortest Path Segmentation Methods to 3D 2008 ·IEEE Transactions on Pattern Analysis and Machine Intelligence ·Leo Grady	34
15	Weights and Topology: A Study of the Effects of Graph Construction on 3D Image Segmentation 2008 ·Lecture notes in computer science ·Leo Grady, Marie‐Pierre Jolly	30
16	Accurate Banded Graph Cut Segmentation of Thin Structures Using Laplacian Pyramids 2006 ·Lecture notes in computer science ·Ali Kemal Sinop, Leo Grady	10
17	Random Walks for Image Segmentation breakdown → 2006 ·IEEE Transactions on Pattern Analysis and Machine Intelligence ·Leo Grady	1728
18	Random walks for interactive alpha-matting 2005 ·Leo Grady, (unknown), (unknown), (unknown)	108
19	Random Walks for Interactive Organ Segmentation in Two and Three Dimensions: Implementation and Validation 2005 ·Lecture notes in computer science ·Leo Grady, (unknown), (unknown), Rüdiger Westermann	82
20	Anisotropic Interpolation on Graphs: The Combinatorial Dirichlet Problem 2003 ·OpenBU/Boston University Institutional Repository (Boston University) ·Leo Grady, Eli Schwartz	15

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.

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