Accepted to NAACL 2025, 2025
Integrated paraphrase supervision in a multimodal pipeline to improve Automatic Speech Recognition for spontaneous and disfluent speech.
Recommended citation: Parulekar, A., Gupta, A., Chattopadhyay, S., & Jyothi, P. (2024). AMPS: ASR with Multimodal Paraphrase Supervision. https://arxiv.org/abs/2411.18368
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Accepted to the 4th Multilingual Representation Learning Workshop, EMNLP 2024, 2024
Combining computationally efficient techniques like speech-based parameter-efficient finetuning and text-only adaptation to improve automatic speech recognition of low resource languages using multimodal multilingual models.
Recommended citation: Abhishek Gupta, Amruta Parulekar, Sameep Chattopadhyay, and Preethi Jyothi. 2024. Parameter-efficient Adaptation of Multilingual Multimodal Models for Low-resource ASR. In Proceedings of the Fourth Workshop on Multilingual Representation Learning (MRL 2024), pages 175–185, Miami, Florida, USA. Association for Computational Linguistics.
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Accepted to IEEE ISBI 2025 (International Symposium on Biomedical Imaging), 2024
Developed PathoGen-X, a transformer-based framework that translates histopathology image features into the genomic feature space for improved survival prediction without requiring paired genomic data at testing.
Recommended citation: Krishna A., Kurian N. C., Patil A., Parulekar A. and Sethi A. (2024). PathoGen-X: A Cross-Modal Genomic Feature Trans-Align Network for Enhanced Survival Prediction from Histopathology Images. https://arxiv.org/abs/2411.00749
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Published and presented in Bioimaging (BIOSTEC), Rome, 2024
A novel loss function and training technique that can be integrated with a multitude of architectures, for consolidating class labels of different nuclei segmentation and classification datasets.
Recommended citation: Parulekar A., Kanwat U., Gupta R., Chippa M., Jacob T., Bameta T., Rane S. and Sethi A. (2024). Combining Datasets with Different Label Sets for Improved Nucleus Segmentation and Classification. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING; ISBN 978-989-758-688-0, SciTePress, pages 281-288. DOI: 10.5220/0012380800003657
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Accepted to the Ultrasonics journal, 2023
Interconversion of CT scans and ultrasounds using wave interference patterns, GANs and fourier domain adaptation.
Recommended citation: Nasser S. A., Sharma A., Saraf A., Parulekar A. M., Haria P. and Sethi A. (2023). Transforming Breast Cancer Diagnosis: Towards Real-Time Ultrasound to Mammogram Conversion for Cost-Effective Diagnosis. https://arxiv.org/abs/2308.05449
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Poster presented in the 145th American Laryngological Association meet (COSM), Chicago, 2024
A preprocessing pipeline for larynogoscopic videos that includes removal of unusable frames, illumination correction, specularity removal and finally color transfer to a target intensity distribution.
Recommended citation: Parulekar A., Wiercigroch J., Kahrs L. A. and Lin R. J. (2024). A Computer Vision Pipeline for Laryngoscopic Image Standardization through Histogram Matching.
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Poster presented in the American Gatroenterology Association meet (DDW), Chicago and published in the Gastroenterology journal, 2023
An eosinophila detection model conquering severe class imbalance built using UNet architecture.
Recommended citation: Shah H.C., Amarpurkar A.D., Jacob T., Parulekar A.M. and Sethi A. (2023). EP178 ARTIFICIAL INTELLIGENCE BASED EOSINOPHIL COUNT IN GASTROINTESTINAL TRACT BIOPSY. Gastroenterology, 164(6), pp.S-1229.
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