Anees Kazi

611 total citations
12 papers, 156 citations indexed

About

Anees Kazi is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Cognitive Neuroscience. According to data from OpenAlex, Anees Kazi has authored 12 papers receiving a total of 156 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Computer Vision and Pattern Recognition, 4 papers in Radiology, Nuclear Medicine and Imaging and 3 papers in Cognitive Neuroscience. Recurrent topics in Anees Kazi's work include Functional Brain Connectivity Studies (3 papers), Health, Environment, Cognitive Aging (3 papers) and Radiomics and Machine Learning in Medical Imaging (2 papers). Anees Kazi is often cited by papers focused on Functional Brain Connectivity Studies (3 papers), Health, Environment, Cognitive Aging (3 papers) and Radiomics and Machine Learning in Medical Imaging (2 papers). Anees Kazi collaborates with scholars based in Germany, United States and United Kingdom. Anees Kazi's co-authors include Nassir Navab, Seyed‐Ahmad Ahmadi, Michael M. Bronstein, Luca Cosmo, Diana Mateus, Sonja Kirchhoff, Shadi Albarqouni, Peter Biberthaler, Chlodwig Kirchhoff and Tanveer Syeda-Mahmood and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Medical Image Analysis and Histochemistry and Cell Biology.

In The Last Decade

Anees Kazi

10 papers receiving 153 citations

Peers

Countries citing papers authored by Anees Kazi

Since Specialization

Citations

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

Fields of papers citing papers by Anees Kazi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Anees Kazi

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

All Works

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12 of 12 papers shown

#	Work	Indexed citations
1	Multi-head Graph Convolutional Network for Structural Connectome Classification 2024 ·Lecture notes in computer science ·Anees Kazi, Jocelyn Mora, Bruce Fischl, Adrian V. Dalca, Iman Aganj	0
2	ReMIND: The Brain Resection Multimodal Imaging Database 2024 ·Scientific Data ·Parikshit Juvekar, Reuben Dorent, (unknown), Erickson Torio, (unknown), Laura Rigolo, (unknown), (unknown), Anees Kazi, Nazim Haouchine, (unknown), Nassir Navab, Steve Pieper, William M. Wells, Wenya Linda Bi, Alexandra J. Golby, Sarah Frisken, Tina Kapur	17
3	IA-GCN: Interpretable Attention Based Graph Convolutional Network for Disease Prediction 2023 ·Lecture notes in computer science ·Anees Kazi, (unknown), Iman Aganj, Nassir Navab	2
4	Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications 2023 ·Medical Image Analysis ·(unknown), Luca Cosmo, Anees Kazi, Seyed‐Ahmad Ahmadi, Michael M. Bronstein, Nassir Navab	5
5	Differentiable Graph Module (DGM) for Graph Convolutional Networks 2022 ·IEEE Transactions on Pattern Analysis and Machine Intelligence ·Anees Kazi, Luca Cosmo, Seyed‐Ahmad Ahmadi, Nassir Navab, Michael M. Bronstein	61
6	DG-GRU: dynamic graph based gated recurrent unit for age and gender prediction using brain imaging 2022 ·Anees Kazi, (unknown), (unknown), (unknown), Ahmed R. Adly, (unknown), Nassir Navab	1
7	Imaging Systems for GI Endoscopy, and Graphs in Biomedical Image Analysis 2022 ·Lecture notes in computer science ·Luigi Manfredi, Seyed‐Ahmad Ahmadi, Michael M. Bronstein, Anees Kazi, (unknown), (unknown), (unknown), (unknown), Bartłomiej W. Papież, Russell H. Taylor, Emanuele Trucco	2
8	Simultaneous imputation and classification using Multigraph Geometric Matrix Completion (MGMC): Application to neurodegenerative disease classification 2021 ·Artificial Intelligence in Medicine ·Gerome Vivar, Anees Kazi, (unknown), Andreas Zwergal, Nassir Navab, Seyed‐Ahmad Ahmadi	10
9	Precise proximal femur fracture classification for interactive training and surgical planning 2020 ·International Journal of Computer Assisted Radiology and Surgery ·(unknown), Anees Kazi, Shadi Albarqouni, Chlodwig Kirchhoff, Peter Biberthaler, Nassir Navab, Sonja Kirchhoff, Diana Mateus	34
10	Endorsing cellular competitiveness in aberrant epithelium of oral submucous fibrosis progression: neighbourhood analysis of immunohistochemical attributes 2018 ·Histochemistry and Cell Biology ·(unknown), Anees Kazi, Mousumi Pal, Ranjan Rashmi Paul, Sanghamitra Sengupta, Jyotirmoy Chatterjee	11
11	Coupled Manifold Learning for Retrieval Across Modalities 2017 ·Anees Kazi, Sailesh Conjeti, Amin Katouzian, Nassir Navab	1
12	Metric hashing forests 2016 ·Medical Image Analysis ·Sailesh Conjeti, Amin Katouzian, Anees Kazi, (unknown), David Beymer, Tanveer Syeda-Mahmood, Nassir Navab	12

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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