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Pritam Mukherjee

Showing results (51-60 of 66) with videos related to

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Arxiv|June 21, 2024
Leveraging Professional Radiologists' Expertise to Enhance LLMs' Evaluation for Radiology ReportsQingqing Zhu, Xiuying Chen, Qiao Jin, et al.
Arxiv|March 26, 2024
Weakly Supervised Detection of Pheochromocytomas and Paragangliomas in CTDavid C Oluigbo, Bikash Santra, Tejas Sudharshan Mathai, et al.
Diabetes|July 9, 2026
Utility of Pancreatic Perivascular Adipose Tissue as a CT Imaging Biomarker for Diagnosing Type 2 DiabetesAnisa V Prasad, Tejas Sudharshan Mathai, Praveen T S Balamuralikrishna, et al.
JCO Clinical Cancer Informatics|July 15, 2021
Machine Learning Radiomics Model for Early Identification of Small-Cell Lung Cancer on Computed Tomography ScansRajesh P Shah, Heather M Selby, Pritam Mukherjee, et al.
Journal of Biomedical Informatics|June 27, 2025
How well do multimodal LLMs interpret CT scans? An auto-evaluation framework for analysesQingqing Zhu, Benjamin Hou, Tejas Sudarshan Mathai, et al.
Nature Machine Intelligence|April 1, 2021
A Shallow Convolutional Neural Network Predicts Prognosis of Lung Cancer Patients in Multi-Institutional CT-Image DataPritam Mukherjee, Mu Zhou, Edward Lee, et al.
Arxiv|October 1, 2025
How Well Do Multi-modal LLMs Interpret CT Scans? An Auto-Evaluation Framework for AnalysesQingqing Zhu, Benjamin Hou, Tejas Sudarshan Mathai, et al.
Radiotherapy and Oncology : Journal of the European Society for Therapeutic Radiology and Oncology|August 22, 2019
Predicting the tumor response to chemoradiotherapy for rectal cancer: Model development and external validation using MRI radiomicsPhilippe Bulens, Alice Couwenberg, Martijn Intven, et al.
Radiology|October 1, 2024
Evaluation of GPT Large Language Model Performance on RSNA 2023 Case of the Day QuestionsPritam Mukherjee, Benjamin Hou, Abhinav Suri, et al.
Arxiv|July 30, 2025
LEAVS: An LLM-based Labeler for Abdominal CT SupervisionRicardo Bigolin Lanfredi, Yan Zhuang, Mark Finkelstein, et al.
Pageof 7

Showing results (51-60 of 66) with videos related to

Sort By:
Pageof 7
Arxiv|June 21, 2024
Leveraging Professional Radiologists' Expertise to Enhance LLMs' Evaluation for Radiology ReportsQingqing Zhu, Xiuying Chen, Qiao Jin, et al.
Arxiv|March 26, 2024
Weakly Supervised Detection of Pheochromocytomas and Paragangliomas in CTDavid C Oluigbo, Bikash Santra, Tejas Sudharshan Mathai, et al.
Diabetes|July 9, 2026
Utility of Pancreatic Perivascular Adipose Tissue as a CT Imaging Biomarker for Diagnosing Type 2 DiabetesAnisa V Prasad, Tejas Sudharshan Mathai, Praveen T S Balamuralikrishna, et al.
JCO Clinical Cancer Informatics|July 15, 2021
Machine Learning Radiomics Model for Early Identification of Small-Cell Lung Cancer on Computed Tomography ScansRajesh P Shah, Heather M Selby, Pritam Mukherjee, et al.
Journal of Biomedical Informatics|June 27, 2025
How well do multimodal LLMs interpret CT scans? An auto-evaluation framework for analysesQingqing Zhu, Benjamin Hou, Tejas Sudarshan Mathai, et al.
Nature Machine Intelligence|April 1, 2021
A Shallow Convolutional Neural Network Predicts Prognosis of Lung Cancer Patients in Multi-Institutional CT-Image DataPritam Mukherjee, Mu Zhou, Edward Lee, et al.
Arxiv|October 1, 2025
How Well Do Multi-modal LLMs Interpret CT Scans? An Auto-Evaluation Framework for AnalysesQingqing Zhu, Benjamin Hou, Tejas Sudarshan Mathai, et al.
Radiotherapy and Oncology : Journal of the European Society for Therapeutic Radiology and Oncology|August 22, 2019
Predicting the tumor response to chemoradiotherapy for rectal cancer: Model development and external validation using MRI radiomicsPhilippe Bulens, Alice Couwenberg, Martijn Intven, et al.
Radiology|October 1, 2024
Evaluation of GPT Large Language Model Performance on RSNA 2023 Case of the Day QuestionsPritam Mukherjee, Benjamin Hou, Abhinav Suri, et al.
Arxiv|July 30, 2025
LEAVS: An LLM-based Labeler for Abdominal CT SupervisionRicardo Bigolin Lanfredi, Yan Zhuang, Mark Finkelstein, et al.
Pageof 7