Ujjwal Baid

2.2k total citations
23 papers, 494 citations indexed

About

Ujjwal Baid is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Neurology. According to data from OpenAlex, Ujjwal Baid has authored 23 papers receiving a total of 494 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Radiology, Nuclear Medicine and Imaging, 8 papers in Computer Vision and Pattern Recognition and 7 papers in Neurology. Recurrent topics in Ujjwal Baid's work include Radiomics and Machine Learning in Medical Imaging (9 papers), Brain Tumor Detection and Classification (7 papers) and Medical Image Segmentation Techniques (4 papers). Ujjwal Baid is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (9 papers), Brain Tumor Detection and Classification (7 papers) and Medical Image Segmentation Techniques (4 papers). Ujjwal Baid collaborates with scholars based in India, United States and Germany. Ujjwal Baid's co-authors include Sanjay N. Talbar, Abhishek Mahajan, Sudeep Gupta, Swapnil Rane, Aliasgar Moiyadi, Meenakshi Thakur, Spyridon Bakas, Bhakti Baheti, Nilesh Sable and Soonmee Cha and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Physics in Medicine and Biology.

In The Last Decade

Ujjwal Baid

21 papers receiving 473 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ujjwal Baid India	11	301	150	138	127	112	23	494
Michelle Bardis United States	8	370 1.2×	92 0.6×	104 0.8×	132 1.0×	151 1.3×	12	576
Biswajit Jena India	10	202 0.7×	129 0.9×	107 0.8×	127 1.0×	43 0.4×	21	431
Zeju Li China	12	489 1.6×	131 0.9×	109 0.8×	155 1.2×	125 1.1×	24	684
Ahmed Alksas United States	10	211 0.7×	67 0.4×	49 0.4×	73 0.6×	72 0.6×	40	330
Jianhong Cheng China	14	193 0.6×	146 1.0×	101 0.7×	124 1.0×	63 0.6×	30	589
Marwa Ismail United States	9	319 1.1×	73 0.5×	51 0.4×	52 0.4×	51 0.5×	33	419
Guidong Song China	9	216 0.7×	418 2.8×	431 3.1×	171 1.3×	44 0.4×	13	831
Pantelis Georgiadis Greece	10	152 0.5×	117 0.8×	111 0.8×	80 0.6×	27 0.2×	22	386
Fatih Incekara Netherlands	13	304 1.0×	122 0.8×	74 0.5×	34 0.3×	67 0.6×	20	605
Oskar Maier Germany	8	168 0.6×	220 1.5×	188 1.4×	80 0.6×	68 0.6×	12	624

Countries citing papers authored by Ujjwal Baid

Since Specialization

Citations

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

Fields of papers citing papers by Ujjwal Baid

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ujjwal Baid

This figure shows the co-authorship network connecting the top 25 collaborators of Ujjwal Baid. A scholar is included among the top collaborators of Ujjwal Baid 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 Ujjwal Baid. Ujjwal Baid 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

Baid, Ujjwal, Sarthak Pati, Tahsin Kurç, et al.. (2024). Pan-Cancer Tumor Infiltrating Lymphocyte Detection based on Federated Learning. 7640–7647.

Mahajan, Abhishek, Ujjwal Baid, Sanjay N. Talbar, et al.. (2023). Deep learning based automated epidermal growth factor receptor and anaplastic lymphoma kinase status prediction of brain metastasis in non-small cell lung cancer. SHILAP Revista de lepidopterología. 4(4). 657–668. 6 indexed citations

Bakas, Spyridon, et al.. (2023). Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Lecture notes in computer science.

Innani, Shubham, Ujjwal Baid, Spyridon Bakas, et al.. (2023). Generative adversarial networks based skin lesion segmentation. Scientific Reports. 13(1). 13467–13467. 15 indexed citations

Bakas, Spyridon, et al.. (2023). Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Lecture notes in computer science. 8 indexed citations

Pati, Sarthak, Ujjwal Baid, Brandon Edwards, et al.. (2022). The federated tumor segmentation (FeTS) tool: an open-source solution to further solid tumor research. Physics in Medicine and Biology. 67(20). 204002–204002. 17 indexed citations

Paul, Sudip, et al.. (2022). Modified U-Net for fully automatic liver segmentation from abdominal CT-image. International Journal of Biomedical Engineering and Technology. 40(1). 1–1. 3 indexed citations

Innani, Shubham, et al.. (2021). Fuse-PN: A Novel Architecture for Anomaly Pattern Segmentation in Aerial Agricultural Images. 2954–2962. 6 indexed citations

Chaudhary, Shubham, et al.. (2021). Detecting Covid-19 and Community Acquired Pneumonia Using Chest CT Scan Images With Deep Learning. 8583–8587. 22 indexed citations

10.

Baid, Ujjwal, et al.. (2021). Deep Residual Separable Convolutional Neural Network for lung tumor segmentation. Computers in Biology and Medicine. 141. 105161–105161. 35 indexed citations

11.

Baid, Ujjwal, et al.. (2021). LNCDS: A 2D-3D cascaded CNN approach for lung nodule classification, detection and segmentation. Biomedical Signal Processing and Control. 67. 102527–102527. 66 indexed citations

12.

Gogoi, Manashjit, et al.. (2020). Cascaded Dilated Deep Residual Network for Volumetric Liver Segmentation From CT Image. International Journal of E-Health and Medical Communications. 12(1). 34–45. 9 indexed citations

13.

Baid, Ujjwal, Sanjay N. Talbar, Swapnil Rane, et al.. (2020). A Novel Approach for Fully Automatic Intra-Tumor Segmentation With 3D U-Net Architecture for Gliomas. Frontiers in Computational Neuroscience. 14. 10–10. 77 indexed citations

14.

Baid, Ujjwal, Swapnil Rane, Sanjay N. Talbar, et al.. (2020). Overall Survival Prediction in Glioblastoma With Radiomic Features Using Machine Learning. Frontiers in Computational Neuroscience. 14. 61–61. 92 indexed citations

15.

Baid, Ujjwal, et al.. (2019). Detection of Pathological Myopia and Optic Disc Segmentation with Deep Convolutional Neural Networks. 1345–1350. 16 indexed citations

16.

Baid, Ujjwal, et al.. (2017). Brain Tumor Segmentation Based on Non Negative Matrix Factorization and Fuzzy Clustering. 134–139. 3 indexed citations

17.

Baid, Ujjwal, et al.. (2017). Comparative Study of K-means, Gaussian Mixture Model, Fuzzy C-means algorithms for Brain Tumor Segmentation. 16 indexed citations

18.

Baid, Ujjwal, et al.. (2017). Novel approach for brain tumor segmentation with non negative matrix factorization. 101–105. 1 indexed citations

19.

Baheti, Bhakti, Ujjwal Baid, & Sanjay N. Talbar. (2016). An approach to automatic object tracking system by combination of SIFT and RANSAC with mean shift and KLT. 254–259. 11 indexed citations

20.

Baheti, Bhakti, Ujjwal Baid, & Sanjay N. Talbar. (2015). A novel approach for Automatic Image Stitching of spinal cord MRI images using SIFT. 4. 1–5. 4 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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