Shubham Innani

594 total citations
15 papers, 295 citations indexed

About

Shubham Innani is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Genetics. According to data from OpenAlex, Shubham Innani has authored 15 papers receiving a total of 295 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Radiology, Nuclear Medicine and Imaging, 7 papers in Artificial Intelligence and 4 papers in Genetics. Recurrent topics in Shubham Innani's work include Radiomics and Machine Learning in Medical Imaging (10 papers), AI in cancer detection (7 papers) and Glioma Diagnosis and Treatment (4 papers). Shubham Innani is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (10 papers), AI in cancer detection (7 papers) and Glioma Diagnosis and Treatment (4 papers). Shubham Innani collaborates with scholars based in United States and India. Shubham Innani's co-authors include Bhakti Baheti, Sanjay N. Talbar, Suhas Gajre, Spyridon Bakas, Ujjwal Baid, Sharath Chandra Guntuku, William R. Bell, MacLean P. Nasrallah and Mona Nasrallah and has published in prestigious journals such as Cancer Research, Scientific Reports and Pattern Recognition Letters.

In The Last Decade

Shubham Innani

12 papers receiving 281 citations

Peers

Shubham Innani

CVPR

RNMI

Olivier Rukundo Switzerland

Zhengeng Yang China

Kun Huang China

Hatice Çatal Reis Türkiye

Amal Rannen Triki Belgium

Jing Dong China

Muhammad Mohsin Khan Pakistan

Kevis-Kokitsi Maninis Switzerland

Olivier Rukundo Switzerland

Shubham Innani

134 ×1.0

CVPR

48 ×0.7

36 ×0.7

RNMI

23 ×0.6

44 ×1.3

Citations per year, relative to Shubham Innani Shubham Innani (= 1×) peers Olivier Rukundo

Countries citing papers authored by Shubham Innani

Since Specialization

Citations

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

Fields of papers citing papers by Shubham Innani

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shubham Innani

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

All Works

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15 of 15 papers shown

Innani, Shubham, et al.. (2025). AI-driven WHO 2021 classification of gliomas based only on H&E-stained slides. Neuro-Oncology. 28(1). 282–296.

Innani, Shubham, et al.. (2025). Interpretable artificial intelligence based determination of glioma IDH mutation status directly from histology slides. Neuro-Oncology Advances. 7(1). vdaf140–vdaf140.

Baheti, Bhakti, Shubham Innani, William R. Bell, et al.. (2025). Multimodal Explainable Artificial Intelligence for Prognostic Stratification of Patients With Glioblastoma. Modern Pathology. 38(9). 100797–100797. 1 indexed citations

Innani, Shubham, et al.. (2025). Abstract 6247: Artificial intelligence predicts 2021 WHO glioma subtypes from whole slide images. Cancer Research. 85(8_Supplement_1). 6247–6247. 1 indexed citations

Innani, Shubham, et al.. (2024). Prognostic stratification of glioblastoma patients by unsupervised clustering of morphology patterns on whole slide images furthering our disease understanding. Frontiers in Neuroscience. 18. 1304191–1304191. 3 indexed citations

Innani, Shubham, et al.. (2024). PATH-39. AI-BASED IDENTIFICATION OF GLIOMA IDH MUTATIONAL STATUS FROM H&E-STAINED WHOLE SLIDE IMAGES. Neuro-Oncology. 26(Supplement_8). viii187–viii187. 1 indexed citations

Innani, Shubham, et al.. (2024). Weakly Supervised IDH-Status Glioma Classification from H&E-Stained Whole Slide Images. 1–5.

Innani, Shubham, et al.. (2023). P13.13.B INTERPRETABLE WHOLE SLIDE IMAGE PROGNOSTIC STRATIFICATION OF GLIOBLASTOMA PATIENTS FURTHERING DISEASE UNDERSTANDING. Neuro-Oncology. 25(Supplement_2). ii103–ii104. 2 indexed citations

Innani, Shubham, et al.. (2023). PATH-39. INTERPRETABLE IDH CLASSIFICATION FROM H&E-STAINED HISTOLOGY SLIDES. Neuro-Oncology. 25(Supplement_5). v177–v177. 2 indexed citations

10.

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

11.

Innani, Shubham, et al.. (2023). OS03.6.A UNSUPERVISED CLUSTERING OF MORPHOLOGY PATTERNS ON WHOLE SLIDE IMAGES GUIDE PROGNOSTIC STRATIFICATION OF GLIOBLASTOMA PATIENTS. Neuro-Oncology. 25(Supplement_2). ii15–ii15. 2 indexed citations

12.

Innani, Shubham, et al.. (2023). EPCO-15. DETECTING HISTOLOGIC & CLINICAL GLIOBLASTOMA PATTERNS OF PROGNOSTIC RELEVANCE. Neuro-Oncology. 25(Supplement_5). v126–v126. 2 indexed citations

13.

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

14.

Baheti, Bhakti, Shubham Innani, Suhas Gajre, & Sanjay N. Talbar. (2020). Eff-UNet: A Novel Architecture for Semantic Segmentation in Unstructured Environment. 1473–1481. 165 indexed citations

15.

Baheti, Bhakti, Shubham Innani, Suhas Gajre, & Sanjay N. Talbar. (2020). Semantic scene segmentation in unstructured environment with modified DeepLabV3+. Pattern Recognition Letters. 138. 223–229. 95 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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