Publications
147
Citations
2,378
Est. group size
—
Recurring co-author estimate
Active years
12
Publishing since 2015
Ujjwal Baid works on applying artificial intelligence and machine learning to medical imaging, with a strong focus on automatically detecting and outlining brain tumors in MRI scans. Much of the work involves organizing and analyzing large public benchmark challenges (such as BraTS) that let researchers compare tumor-segmentation algorithms, including efforts to include underrepresented patient populations and to train models across hospitals without sharing raw data (federated learning). Related projects extend these methods to other cancers and to traumatic brain injury.
Publication activity grew sharply from around 2020 and has remained high, averaging roughly 18 papers per year over the last five years.
Generated by claude-opus-4-8 from public bibliographic data · Jul 11, 2026
- BIO26-046: Validating an LLM for Detecting Enrollment Disparities in Breast Cancer Trials
Journal of the National Comprehensive Cancer Network · 2026
- My Model Is Better Than Yours! Statistically-Aware Ranking for Fair Benchmarking of AI Models
Lecture notes in computer science · 2026
- Abstract 82: Artificial intelligence derived spatial transcriptomic signatures from H&E slides predict survival in primary melanoma.
Cancer Research · 2026
- Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge
arXiv (Cornell University) · 2026
- Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge
arXiv (Cornell University) · 2026
- BraTS 2026 Cluster of Challenges:
Zenodo (CERN European Organization for Nuclear Research) · 2026
- BraTS 2026 Cluster of Challenges:
Zenodo (CERN European Organization for Nuclear Research) · 2026
- AIMS-TBI : Automated Identification of Moderate-Severe Traumatic Brain Injury Lesions
Zenodo (CERN European Organization for Nuclear Research) · 2026
- AIMS-TBI : Automated Identification of Moderate-Severe Traumatic Brain Injury Lesions
Zenodo (CERN European Organization for Nuclear Research) · 2026
- Brain tumor segmentation in Sub-Saharan Africa patient population: The BraTS-Africa challenge
Neuro-Oncology Advances · 2026
- The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI
The Journal of Machine Learning for Biomedical Imaging · 2025
- Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge
The Journal of Machine Learning for Biomedical Imaging · 2025
- Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge
Nature Communications · 2025
- Abstract 2455: AI-based robust testicular cancer triaging, a promise for an efficient and cost-effective diagnostic/predictive modality
Cancer Research · 2025
- IMG-66. Self-supervised multimodal learning for survival prediction in glioblastoma: a multicenter study from the ReSPOND consortium
Neuro-Oncology · 2025
- arXiv (Cornell University)×35
- Neuro-Oncology×16
- Lecture notes in computer science×6
- PubMed×6
- Zenodo (CERN European Organization for Nuclear Research)×6
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 11, 2026.
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