Papers by Shantanu Ghosh
Semantic Consistency-Based Uncertainty Quantification for Factuality in Radiology Report Generation (2025.findings-naacl)
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| Challenge: | Radiology report generation has shown great potential in assisting radiologists . generative medical Vision Large Language Models (VLLMs) are prone to hallucinations and can produce inaccurate diagnostic information. |
| Approach: | They propose a framework that provides both report-level and sentence-level uncertainties. |
| Outcome: | The proposed method improves factuality scores by 10% by rejecting 20% of reports on the MIMIC-CXR dataset. |
LADDER: Language-Driven Slice Discovery and Error Rectification in Vision Classifiers (2025.findings-acl)
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Shantanu Ghosh, Rayan Syed, Chenyu Wang, Vaibhav Choudhary, Binxu Li, Clare B Poynton, Shyam Visweswaran, Kayhan Batmanghelich
| Challenge: | Current slice discovery methods in computer vision rely on converting input images into sets of attributes and testing hypotheses about configurations of pre-computed attributes associated with elevated error patterns. |
| Approach: | They propose a method to identify systematic biases in the mistakes of pre-trained vision models by converting input images into sets of attributes and testing hypotheses about configurations of these attributes. |
| Outcome: | The proposed method outperforms existing methods on 3 natural and 3 medical imaging datasets and generates pseudo-labels for each identified bias. |