Shaury Srivastav
2024
MAIRA at RRG24: A specialised large multimodal model for radiology report generation
Shaury Srivastav
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Mercy Ranjit
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Fernando Pérez-García
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Kenza Bouzid
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Shruthi Bannur
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Daniel C. Castro
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Anton Schwaighofer
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Harshita Sharma
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Maximilian Ilse
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Valentina Salvatelli
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Sam Bond-Taylor
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Fabian Falck
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Anja Thieme
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Hannah Richardson
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Matthew P. Lungren
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Stephanie L. Hyland
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Javier Alvarez-Valle
Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
This paper discusses the participation of the MSR MAIRA team in the Large-Scale Radiology Report Generation Shared Task Challenge, as part of the BioNLP workshop at ACL 2024. We present a radiology-specific multimodal model designed to generate radiological reports from chest X-Rays (CXRs). Our proposed model combines a CXR-specific image encoder RAD-DINO with a Large Language Model (LLM) based on Vicuna-7B, via a multi-layer perceptron (MLP) adapter. Both the adapter and the LLM have been fine-tuned in a single-stage training setup to generate radiology reports. Experimental results indicate that a joint training setup with findings and impression sections improves findings prediction. Additionally, incorporating lateral images alongside frontal images when available further enhances all metrics. More information and resources about MAIRA can be found on the project website: http://aka.ms/maira.
2022
Bias, Threat and Aggression Identification Using Machine Learning Techniques on Multilingual Comments
Kirti Kumari
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Shaury Srivastav
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Rajiv Ranjan Suman
Proceedings of the Third Workshop on Threat, Aggression and Cyberbullying (TRAC 2022)
In this paper, we presented our team "IIITRanchi” for the Trolling, Aggression and Cyberbullying (TRAC-3) 2022 shared tasks. Aggression and its different forms on social media and other platforms had tremendous growth on the Internet. In this work we have tried upon different aspects of aggression, aggression intensity, bias of different forms and their usage online and its identification using different Machine Learning techniques. We have classified each sample at seven different tasks namely aggression level, aggression intensity, discursive role, gender bias, religious bias, caste/class bias and ethnicity/racial bias as specified in the shared tasks. Both of our teams tried machine learning classifiers and achieved the good results. Overall, our team "IIITRanchi” ranked first position in this shared tasks competition.
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Co-authors
- Mercy Ranjit 1
- Fernando Pérez-García 1
- Kenza Bouzid 1
- Shruthi Bannur 1
- Daniel C. Castro 1
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