B Bhattarai MultiModal Learning Lab (MMLL)

@article{bano2023placental,
  title={Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings},
  author={Bano, Sophia and Casella, Alessandro and Vasconcelos, Francisco and Qayyum, Abdul and Benzinou, Abdesslam and Mazher, Moona and Meriaudeau, Fabrice and Lena, Chiara and Cintorrino, Ilaria Anita and De Paolis, Gaia Romana and others},
  journal={Medical Image Analysis},
  year={2023}
}

@article{bhattarai2023histogram,
  title={Histogram of Oriented Gradients meet deep learning: A novel multi-task deep network for 2D surgical image semantic segmentation},
  author={Bhattarai, Binod and Subedi, Ronast and Gaire, Rebati Raman and Vazquez, Eduard and Stoyanov, Danail},
  journal={Medical Image Analysis},
  pages={102747},
  year={2023},
  publisher={Elsevier}
}

@inproceedings{timilsina-etal-2022-nepberta,
    title = "{N}ep{BERT}a: {N}epali Language Model Trained in a Large Corpus",
    author = "Timilsina, Sulav  and
      Gautam, Milan  and
      Bhattarai, Binod",
    booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2022",
    address = "Online only",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.aacl-short.34",
    pages = "273--284",
    abstract = "Nepali is a low-resource language with more than 40 million speakers worldwide. It is written in Devnagari script and has rich semantics and complex grammatical structure. To this date, multilingual models such as Multilingual BERT, XLM and XLM-RoBERTa haven{'}t been able to achieve promising results in Nepali NLP tasks, and there does not exist any such a large-scale monolingual corpus. This study presents NepBERTa, a BERT-based Natural Language Understanding (NLU) model trained on the most extensive monolingual Nepali corpus ever. We collected a dataset of 0.8B words from 36 different popular news sites in Nepal and introduced the model. This data set is 3 folds times larger than the previous publicly available corpus. We evaluated the performance of NepBERTa in multiple Nepali-specific NLP tasks, including Named-Entity Recognition, Content Classification, POS Tagging, and Sequence Pair Similarity. We also introduce two different datasets for two new downstream tasks and benchmark four diverse NLU tasks altogether. We bring all these four tasks under the first-ever Nepali Language Understanding Evaluation (Nep-gLUE) benchmark. We will make Nep-gLUE along with the pre-trained model and data sets publicly available for research.",
}