ResearcherMayo ClinicActive in the last 2 years
Sovanlal Mukherjee
Matched on ORCID iD 0000-0002-8238-4794; OpenAlex author ID A5037241754. Sources: OpenAlex.
Their work
- Papers
- 7 (first author 2, last author 0)
- Trials
- 0
- Grants
- 0
- Labs led
- 0
- Collaborators
- 16
2022: 1 paper · 2023: 1 paper · 2024: 1 paper · 2025: 2 papers · 2026: 2 papers
Papers 7
- Anatomically constrained deep learning for clinical-grade volumetric pancreatic cancer segmentation: development, validation, and architectural benchmarking. Co-author DOI 10.1038/s41698-026-01587-8↗
- Next-generation AI for visually occult pancreatic cancer detection in a low-prevalence setting with longitudinal stability and multi-institutional generalisabi… Co-author DOI 10.1136/gutjnl-2025-337266↗
- AI-Driven insights in pancreatic cancer imaging: from pre-diagnostic detection to prognostication. First author DOI 10.1007/s00261-024-04775-x↗
- Artificial Intelligence-Augmented Imaging for Early Pancreatic Cancer Detection. First author DOI 10.1159/000546603↗
- Assessing the robustness of a machine-learning model for early detection of pancreatic adenocarcinoma (PDA): evaluating resilience to variations in image acqui… Co-author DOI 10.1007/s00261-023-04127-1↗
- Bounding box-based 3D AI model for user-guided volumetric segmentation of pancreatic ductal adenocarcinoma on standard-of-care CTs. Co-author DOI 10.1016/j.pan.2023.05.008↗
- Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diag… Co-author DOI 10.1053/j.gastro.2022.06.066↗
Closest collaborators
The 16 strongest of 16 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.
- Ajit Harishkumar Goenka · 7 shared papers, 2022–2026 · strength 1.61
- Paper Artificial Intelligence-Augmented Imaging for Early Pancreatic Cancer Detection. DOI 10.1159/000546603↗
- Paper Anatomically constrained deep learning for clinical-grade volumetric pancreatic cancer segmentation: development, valid… DOI 10.1038/s41698-026-01587-8↗
- Paper Next-generation AI for visually occult pancreatic cancer detection in a low-prevalence setting with longitudinal stabil… DOI 10.1136/gutjnl-2025-337266↗
- Ajith Antony · 4 shared papers, 2025–2026 · strength 1.11
- Paper Artificial Intelligence-Augmented Imaging for Early Pancreatic Cancer Detection. DOI 10.1159/000546603↗
- Paper Anatomically constrained deep learning for clinical-grade volumetric pancreatic cancer segmentation: development, valid… DOI 10.1038/s41698-026-01587-8↗
- Paper Next-generation AI for visually occult pancreatic cancer detection in a low-prevalence setting with longitudinal stabil… DOI 10.1136/gutjnl-2025-337266↗
- Suresh T. Chari · 4 shared papers, 2022–2026 · strength 0.82
- Paper Anatomically constrained deep learning for clinical-grade volumetric pancreatic cancer segmentation: development, valid… DOI 10.1038/s41698-026-01587-8↗
- Paper Next-generation AI for visually occult pancreatic cancer detection in a low-prevalence setting with longitudinal stabil… DOI 10.1136/gutjnl-2025-337266↗
- Paper Bounding box-based 3D AI model for user-guided volumetric segmentation of pancreatic ductal adenocarcinoma on standard-… DOI 10.1016/j.pan.2023.05.008↗
- Aashna Karbhari · 3 shared papers, 2023–2026 · strength 0.64
- Paper Next-generation AI for visually occult pancreatic cancer detection in a low-prevalence setting with longitudinal stabil… DOI 10.1136/gutjnl-2025-337266↗
- Paper Assessing the robustness of a machine-learning model for early detection of pancreatic adenocarcinoma (PDA): evaluating… DOI 10.1007/s00261-023-04127-1↗
- Paper Bounding box-based 3D AI model for user-guided volumetric segmentation of pancreatic ductal adenocarcinoma on standard-… DOI 10.1016/j.pan.2023.05.008↗
- Garima Suman · 3 shared papers, 2022–2024 · strength 0.50
- Paper Assessing the robustness of a machine-learning model for early detection of pancreatic adenocarcinoma (PDA): evaluating… DOI 10.1007/s00261-023-04127-1↗
- Paper Bounding box-based 3D AI model for user-guided volumetric segmentation of pancreatic ductal adenocarcinoma on standard-… DOI 10.1016/j.pan.2023.05.008↗
- Paper Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Su… DOI 10.1053/j.gastro.2022.06.066↗
- Mark Joseph Truty · 2 shared papers, 2023–2026 · strength 0.41
- Paper Next-generation AI for visually occult pancreatic cancer detection in a low-prevalence setting with longitudinal stabil… DOI 10.1136/gutjnl-2025-337266↗
- Paper Bounding box-based 3D AI model for user-guided volumetric segmentation of pancreatic ductal adenocarcinoma on standard-… DOI 10.1016/j.pan.2023.05.008↗
- Hala Khasawneh · 2 shared papers, 2022–2023 · strength 0.28
- Paper Bounding box-based 3D AI model for user-guided volumetric segmentation of pancreatic ductal adenocarcinoma on standard-… DOI 10.1016/j.pan.2023.05.008↗
- Paper Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Su… DOI 10.1053/j.gastro.2022.06.066↗
- Shounak Majumder · 2 shared papers, 2022–2023 · strength 0.28
- Paper Bounding box-based 3D AI model for user-guided volumetric segmentation of pancreatic ductal adenocarcinoma on standard-… DOI 10.1016/j.pan.2023.05.008↗
- Paper Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Su… DOI 10.1053/j.gastro.2022.06.066↗
- Rondell Patrell Graham · 1 shared paper, 2026 · strength 0.27
- Eric Andrew Collisson · 1 shared paper, 2025 · strength 0.27
- Michael B. Wallace · 1 shared paper, 2025 · strength 0.27
- Yan Bi · 1 shared paper, 2025 · strength 0.27
- Thorvardur Ragnar Halfdanarson · 1 shared paper, 2023 · strength 0.14
- Kumaresan Sandrasegaran · 1 shared paper, 2023 · strength 0.14
- Candice W. Bolan · 1 shared paper, 2023 · strength 0.14
- Ananya Panda · 1 shared paper, 2022 · strength 0.13
How to reach this person
A chain of introductions through people who have published, run trials or held grants together (warm_path@1: fewest links first, then the strongest). Built from public records only; no contact details are kept.