Manohar Karki

856 total citations
11 papers, 510 citations indexed

About

Manohar Karki is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Manohar Karki has authored 11 papers receiving a total of 510 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 3 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Manohar Karki's work include Machine Learning in Healthcare (2 papers), Intracerebral and Subarachnoid Hemorrhage Research (2 papers) and AI in cancer detection (2 papers). Manohar Karki is often cited by papers focused on Machine Learning in Healthcare (2 papers), Intracerebral and Subarachnoid Hemorrhage Research (2 papers) and AI in cancer detection (2 papers). Manohar Karki collaborates with scholars based in United States, South Korea and China. Manohar Karki's co-authors include Robert DiBiano, Saikat Basu, Sangram Ganguly, Supratik Mukhopadhyay, Ramakrishna Nemani, Eunmi Lee, Sin-Youl Park, Jeong-Woo Son, Junghwan Cho and Ki‐Su Park and has published in prestigious journals such as IEEE Transactions on Geoscience and Remote Sensing, Neural Networks and Artificial Intelligence in Medicine.

In The Last Decade

Manohar Karki

10 papers receiving 499 citations

Peers

Manohar Karki

CVPR

MT

AI

NEURO

ECOLO

David Acuna Canada

Yanfeng Shang China

Tianming Zhan China

Shilpa Suresh India

Yiming Sun China

Bin Tian China

Sachith Seneviratne Australia

Bowen Cheng United States

Esam Othman Saudi Arabia

David Acuna Canada

Manohar Karki

603 ×3.7

CVPR

126 ×0.8

MT

207 ×1.8

AI

6 ×0.1

NEURO

22 ×0.3

ECOLO

Citations per year, relative to Manohar Karki Manohar Karki (= 1×) peers David Acuna

Countries citing papers authored by Manohar Karki

Since Specialization

Citations

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

Fields of papers citing papers by Manohar Karki

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Manohar Karki

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

All Works

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11 of 11 papers shown

1.

Karki, Manohar, Feng Yang, Hang Yu, et al.. (2022). Generalization Challenges in Drug-Resistant Tuberculosis Detection from Chest X-rays. Diagnostics. 12(1). 188–188. 19 indexed citations

2.

Yang, Feng, Hang Yu, Manohar Karki, et al.. (2021). Differentiating between drug-sensitive and drug-resistant tuberculosis with machine learning for clinical and radiological features. Quantitative Imaging in Medicine and Surgery. 12(1). 675–687. 17 indexed citations

3.

Karki, Manohar, Junghwan Cho, Eunmi Lee, et al.. (2020). CT window trainable neural network for improving intracranial hemorrhage detection by combining multiple settings. Artificial Intelligence in Medicine. 106. 101850–101850. 31 indexed citations

4.

Cho, Junghwan, Ki‐Su Park, Manohar Karki, et al.. (2019). Improving Sensitivity on Identification and Delineation of Intracranial Hemorrhage Lesion Using Cascaded Deep Learning Models. Journal of Digital Imaging. 32(3). 450–461. 93 indexed citations

5.

Basu, Saikat, Supratik Mukhopadhyay, Manohar Karki, et al.. (2017). Deep neural networks for texture classification—A theoretical analysis. Neural Networks. 97. 173–182. 56 indexed citations

6.

Basu, Saikat, Manohar Karki, Sangram Ganguly, et al.. (2016). Learning Sparse Feature Representations Using Probabilistic Quadtrees and Deep Belief Nets. Neural Processing Letters. 45(3). 855–867. 46 indexed citations

7.

Karki, Manohar, et al.. (2016). A symbolic framework for recognizing activities in full motion surveillance videos. Civil War Book Review. 1–7. 2 indexed citations

8.

Basu, Saikat, Sangram Ganguly, Supratik Mukhopadhyay, et al.. (2015). DeepSat. Civil War Book Review. 1–10. 208 indexed citations

9.

Basu, Saikat, et al.. (2015). An Agile Framework for Real-Time Motion Tracking. Civil War Book Review. 1. 205–210. 1 indexed citations

10.

Basu, Saikat, Manohar Karki, Sangram Ganguly, et al.. (2015). Learning Sparse Feature Representations using Probabilistic Quadtrees and Deep Belief Nets. arXiv (Cornell University). 4 indexed citations

11.

Basu, Saikat, Sangram Ganguly, Ramakrishna Nemani, et al.. (2015). A Semiautomated Probabilistic Framework for Tree-Cover Delineation From 1-m NAIP Imagery Using a High-Performance Computing Architecture. IEEE Transactions on Geoscience and Remote Sensing. 53(10). 5690–5708. 33 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.

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