Publications

NepBERTa: Nepali Language Model Trained in a Large Corpus 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 - AACL-IJCNLP 2022

COVID-19-related Nepali Tweets Classification in a Low Resource Setting Proceedings of The Seventh Workshop on Social Media Mining for Health Applications (SMM4H), Workshop & Shared Task, COLING 2022, Korea2022.smm4h-1.52

TGANet: Text-guided attention for improved polyp segmentation Medical Image Computing and Computer Assisted Intervention – MICCAITGANet: Text-guided attention for improved polyp segmentation

Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

FixMatchSeg: Fixing FixMatch for Semi-Supervised Semantic Segmentation FixMatchSeg: Fixing FixMatch for Semi-Supervised Semantic Segmentation

Histogram of Oriented Gradients Meet Deep Learning: A Novel Multi-task Deep Network for Medical Image Semantic Segmentation Histogram of Oriented Gradients Meet Deep Learning: A Novel Multi-task Deep Network for Medical Image Semantic Segmentation

Task-Aware Active Learning for Endoscopic Image Analysis Task-Aware Active Learning for Endoscopic Image Analysis

FetReg2021: A Challenge on Placental Vessel Segmentation and Registration in Fetoscopy FetReg2021: A Challenge on Placental Vessel Segmentation and Registration in Fetoscopy

Noisy Heuristics NAS: A Network Morphism based Neural Architecture Search using Heuristics Dynamic Neural Networks, ICMLNoisy Heuristics NAS: A Network Morphism based Neural Architecture Search using Heuristics

Label Geometry Aware Discriminator for Conditional Generative Networks ICPR 2022

Challenges of Deep Learning Methods for COVID-19 Detection Using Public Datasets Informatics in Medicine UnlockedChallenges of Deep Learning Methods for COVID-19 Detection Using Public Datasets

Iterative deep learning for improved segmentation of endoscopic images Nordic Machine IntelligenceIterative deep learning for improved segmentation of endoscopic images

Visualising Argumentation Graphs with Graph Embeddings and t-SNE Visualising Argumentation Graphs with Graph Embeddings and t-SNE

Penalizing small errors using an Adaptive Logarithmic Loss International Conference on Pattern Recognition, Springer, ChamPenalizing small errors using an Adaptive Logarithmic Loss

FatNet: A feature-attentive network for 3D point cloud processing IEEE, International Conference on Pattern Recognition (ICPR)FatNet: A feature-attentive network for 3D point cloud processing

Evaluation and comparison of accurate automated spinal curvature estimation algorithms with spinal anterior-posterior X-Ray images: The AASCE2019 challenge Medical Image AnalysisEvaluation and comparison of accurate automated spinal curvature estimation algorithms with spinal anterior-posterior X-Ray images: The AASCE2019 challenge

Machine-Learning-Assisted Analysis of Colorimetric Assays on Paper Analytical Devices ACS OmegaMachine-Learning-Assisted Analysis of Colorimetric Assays on Paper Analytical Devices

Input Invex Neural Network Input Invex Neural Network

Evaluation and Comparison of Accurate Automated Spinal Curvature Estimation Algorithms with Spinal Anterior-posterior X-Ray Images: The AASCE2019 Challenge Medical Image AnalysisEvaluation and Comparison of Accurate Automated Spinal Curvature Estimation Algorithms with Spinal Anterior-posterior X-Ray Images: The AASCE2019 Challenge

COVID-19 control strategies and intervention effects in resource limited settings: a modeling study PLOS ONECOVID-19 control strategies and intervention effects in resource limited settings: a modeling study

Towards automated extraction of 2D standard fetal head planes from 3D ultrasound acquisitions: A clinical evaluation and quality assessment comparison Radiography (Journal)

Determining the Acceptability of Abstract Arguments with Graph Convolutional Networks [email protected] COMMADetermining the Acceptability of Abstract Arguments with Graph Convolutional Networks

Ensemble U-Net model for efficient polyp segmentation CEUR Workshop proceeding (MediaEval 2020)Ensemble U-Net model for efficient polyp segmentation

Uncertainty Estimation in Deep 2D Echocardiography Segmentation Uncertainty Estimation in Deep 2D Echocardiography Segmentation

Adapted and Oversegmenting Graphs: Application to Geometric Deep Learning Computer Vision, Deep Learning, Artificial Intelligence Under ReviewAdapted and Oversegmenting Graphs: Application to Geometric Deep Learning

Automatic Cobb Angle Detection using Vertebra Detector and Vertebra Corners Regression Medical Imaging, Computer Vision, Machine Learning, Artificial Intelligence Computational Methods and Clinical Applications for Spine Imaging. MICCAI 2019 CSI Workshop & Challenge, Shenzen, China Automatic Cobb Angle Detection using Vertebra Detector and Vertebra Corners Regression

Adapted and Oversegmenting Graphs: Application to Geometric Deep Learning Computer Vision, Deep Learning, Artificial Intelligence Under ReviewAdapted and Oversegmenting Graphs: Application to Geometric Deep Learning

Image Reconstruction in a Manifold of Image Patches: Application to Whole-fetus Ultrasound Imaging MLMIR workshop in MICCAI (accepted)

Confident Head Circumference Measurement from Ultrasound with Real-time Feedback for Sonographers MICCAI 2019 (Accepted, 31% acceptance rate)Confident Head Circumference Measurement from Ultrasound with Real-time Feedback for Sonographers

Towards whole placenta segmentation at late gestation using multi-view ultrasound images MICCAI 2019 (Accepted, 31% acceptance rate)

Complete Fetal Head Compounding from Multi-View 3D Ultrasound MICCAI 2019 (Accepted, 31% acceptance rate)

Bishesh Khanal, Robert and Toussaint, Nicolas and Gomez, Alberto and Zimmer, Veronika and Matthew, Jacqueline and Skelton, Emily and, Bishesh Khanal, and Kainz, Bernhard and Reuckert, Daniel and Hajnal, Jo and Schnabel, Julia
Complete Fetal Head Compounding from Multi-View 3D Ultrasound
MICCAI 2019 (Accepted, 31% acceptance rate), 2019
Bibtex

@inproceedings{wright2019complete,
  title={Complete fetal head compounding from multi-view 3D ultrasound},
  author={Wright, Robert and Toussaint, Nicolas and Gomez, Alberto and Zimmer, Veronika and Khanal, Bishesh and Matthew, Jacqueline and Skelton, Emily and Kainz, Bernhard and Rueckert, Daniel and Hajnal, Joseph V and others},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={384--392},
  year={2019},
  organization={Springer}
}

Zimmer, Veronika and Gomez, Alberto and Skelton, Emily and Toussaint, Nicolas and Zhang, Tong and , Bishesh Khanal, and Wright, Robert and Noh, Yohan and Ho, Alison and Matthew, Jacqueline and Schnabel, Julia
Towards whole placenta segmentation at late gestation using multi-view ultrasound images
MICCAI 2019 (Accepted, 31% acceptance rate), 2019
Bibtex

@inproceedings{zimmer2019towards,
  title={Towards whole placenta segmentation at late gestation using multi-view ultrasound images},
  author={Zimmer, Veronika and Gomez, Alberto and Skelton, Emily and Toussaint, Nicolas and Zhang, Tong and Khanal, Bishesh and Wright, Robert and Noh, Yohan and Ho, Alison and Matthew, Jacqueline and Schnabel, Julia},
  booktitle={MICCAI},
  year={2019},
  note={Accepted}
}

Budd, Samuel and Sinclair, Matthew and, Bishesh Khanal, and Matthew, Jacqueline and Llyod, David and Gomez, Alberto and Toussaint, Nicolas and Robinson, Emma and Kainz, Bernhard
Confident Head Circumference Measurement from Ultrasound with Real-time Feedback for Sonographers
MICCAI 2019 (Accepted, 31% acceptance rate), 2019
PDF Bibtex

@inproceedings{budd2019confident,
  title={Confident Head Circumference Measurement from Ultrasound with Real-time Feedback for Sonographers},
  author={Budd, Samuel and Sinclair, Matthew and Khanal, Bishesh and  Matthew, Jacqueline and Llyod, David and Gomez, Alberto and Toussaint, Nicolas and Robinson, Emma and Kainz, Bernhard},
  booktitle={MICCAI},
  year={2019},
  note={Accepted}
}

Alberto Gomez, Veronika Zimmer, Nicolas Toussaint, Robert Wright, James R. Clough, Bishesh Khanal, Milou Van Poppel, Emily Skelton, Jackie Matthews, and Julia A. Schnabel
Image Reconstruction in a Manifold of Image Patches: Application to Whole-fetus Ultrasound Imaging
MLMIR workshop in MICCAI (accepted), 2019
Bibtex

                  @inproceedings{gomez2019image,
                    title={Image Reconstruction in a Manifold of Image Patches: Application to Whole-fetus Ultrasound Imaging},
                    author={Gomez, Alberto and Zimmer, Veronika and Toussaint, Nicolas and Wright, Robert and Clough, James R. and Khanal, Bishesh and Poppel, Milou Van and Skelton, Emily and Matthews, Jackie and Schnabel, Julia A.},
                    booktitle={MLMIR in MICCAI},
                    year={2019},
		    note={Accepted},                    
                  }

Alberto Gomez, Veronika A. Zimmer, Bishesh Khanal, Nicolas Toussaint, Julia A. Schnabel
Adapted and Oversegmenting Graphs: Application to Geometric Deep Learning
Computer Vision, Deep Learning, Artificial IntelligenceUnder Review, 2019
PDF Bibtex

@article{gomez2018oversegmenting,
  title={Oversegmenting Graphs},
  author={Gomez, Alberto and Zimmer, Veronika A and Khanal, Bishesh and Toussaint, Nicolas and Schnabel, Julia A},
  journal={arXiv preprint arXiv:1806.00411},
  year={2018},
 note={under review}
}

Bidur Khanal, Lavsen Dahal, Prashant Adhikari, Bishesh Khanal
Automatic Cobb Angle Detection using Vertebra Detector and Vertebra Corners Regression
Medical Imaging, Computer Vision, Machine Learning, Artificial IntelligenceComputational Methods and Clinical Applications for Spine Imaging. MICCAI 2019 CSI Workshop & Challenge, Shenzen, China , 2019
PDF Bibtex

@article{khanal2019automatic,
  title={Automatic Cobb Angle Detection using Vertebra Detector and Vertebra Corners Regression},
  author={Khanal, Bidur and Dahal, Lavsen and Adhikari, Prashant and Khanal, Bishesh},
  url={arXiv preprint arXiv:1910.14202},
  year={2019},
  maintitle = {Medical Image Computing and Computer Assisted Intervention (MICCAI) 2019},
  booktitle = {Computational Methods and Clinical Applications for Spine Imaging. CSI Workshop and Challenge}
}

E Skelton, J Matthew, Y Li, Bishesh Khanal, JJ Cerrolaza Martinez, N Toussaint, C Gupta, C Knight, B Kainz, JV Hajnal, M Rutherford
Towards automated extraction of 2D standard fetal head planes from 3D ultrasound acquisitions: A clinical evaluation and quality assessment comparison
Radiography (Journal), 2021
Bibtex

@article{SKELTON2021519,
title = {Towards automated extraction of 2D standard fetal head planes from 3D ultrasound acquisitions: A clinical evaluation and quality assessment comparison},
journal = {Radiography},
volume = {27},
number = {2},
pages = {519-526},
year = {2021},
issn = {1078-8174},
doi = {https://doi.org/10.1016/j.radi.2020.11.006},
url = {https://www.sciencedirect.com/science/article/pii/S1078817420302352},
author = {E. Skelton and J. Matthew and Y. Li and B. Khanal and J.J. {Cerrolaza Martinez} and N. Toussaint and C. Gupta and C. Knight and B. Kainz and J.V. Hajnal and M. Rutherford},
keywords = {Clinical evaluation, Fetal imaging, Quality assessment, Ultrasound},
}

Md Kamrul Hasan, Md Ashraful Alam, Lavsen Dahal, Md Toufick E Elahi, Shidhartho Roy, Sifat Redwan Wahid, Robert Marti, Bishesh Khanal
Challenges of Deep Learning Methods for COVID-19 Detection Using Public Datasets
Informatics in Medicine Unlocked, 2022
PDF Bibtex

@article {Hasan2020.11.07.20227504,
	author = {Hasan, Md. Kamrul and Alam, Md. Ashraful and Dahal, Lavsen and Elahi, Md. Toufick E and Roy, Shidhartho and Wahid, Sifat Redwan and Mart{\'\i}, Robert and Khanal, Bishesh},
	title = {Challenges of Deep Learning Methods for COVID-19 Detection Using Public Datasets},
	elocation-id = {2020.11.07.20227504},
	year = {2020},
	doi = {10.1101/2020.11.07.20227504},
	publisher = {Cold Spring Harbor Laboratory Press},
	
}

Kiran Raj Pandey, Anup Subedee, Bishesh Khanal, Bhagawan Koirala
COVID-19 control strategies and intervention effects in resource limited settings: a modeling study
PLOS ONE, 2021
PDF Bibtex

@article{pandey2021covid,
  title={COVID-19 control strategies and intervention effects in resource limited settings: a modeling study},
  author={Pandey, Kiran Raj and Subedee, Anup and Khanal, Bishesh and Koirala, Bhagawan},
  journal={Plos one},
  volume={16},
  number={6},
  pages={e0252570},
  year={2021},
  publisher={Public Library of Science San Francisco, CA USA}
}

Liansheng Wang, Cong Xie, Yi Lin, Hong-Yu Zhou, Kailin Chen, Dalong Cheng, Florian Dubost, Benjamin Collery, Bidur Khanal, Bishesh Khanal, Rong Tao, Shangliang Xu, Upasana Upadhyay Bharadwaj, Zhusi Zhong, Jie Li, Shuxin Wang, Shuo Li
Evaluation and Comparison of Accurate Automated Spinal Curvature Estimation Algorithms with Spinal Anterior-posterior X-Ray Images: The AASCE2019 Challenge
Medical Image Analysis, 2021
PDF Bibtex

@article{wang2021evaluation,
  title={Evaluation and Comparison of Accurate Automated Spinal Curvature Estimation Algorithms with Spinal Anterior-posterior X-Ray Images: The AASCE2019 Challenge},
  author={Wang, Liansheng and Xie, Cong and Lin, Yi and Zhou, Hong-Yu and Chen, Kailin and Cheng, Dalong and Dubost, Florian and Collery, Benjamin and Khanal, Bidur and Khanal, Bishesh and others},
  journal={Medical Image Analysis},
  pages={102115},
  year={2021},
  publisher={Elsevier}
}

Label Geometry Aware Discriminator for Conditional Generative Networks
ICPR 2022, 2022
PDF Bibtex

@article{sapkota2021label,
  title={Label Geometry Aware Discriminator for Conditional Generative Networks},
  author={Sapkota, Suman and Khanal, Bidur and Bhattarai, Binod and Khanal, Bishesh and Kim, Tae-Kyun},
  journal={arXiv preprint arXiv:2105.05501},
  year={2021}
}

Noisy Heuristics NAS: A Network Morphism based Neural Architecture Search using Heuristics
Dynamic Neural Networks, ICML, 2022
PDF Bibtex

@article{sapkota2022noisy,
  title={Noisy Heuristics NAS: A Network Morphism based Neural Architecture Search using Heuristics},
  author={Sapkota, Suman and Bhattarai, Binod},
  journal={arXiv preprint arXiv:2207.04467},
  year={2022}
}

Sophia Bano, Alessandro Casella, Francisco Vasconcelos, Abdul Qayyum, Abdesslam Benzinou, Moona Mazher, Fabrice Meriaudeau, Chiara Lena, Ilaria Anita Cintorrino, Gaia Romana De Paolis, Jessica Biagioli, Daria Grechishnikova, Jing Jiao, Bizhe Bai, Yanyan Qiao, Binod Bhattarai, Rebati Raman Gaire, Ronast Subedi, Eduard Vazquez, Szymon Płotka, Aneta Lisowska, Arkadiusz Sitek, George Attilakos, Ruwan Wimalasundera, Anna L David, Dario Paladini, Jan Deprest, Elena De Momi, Leonardo S Mattos, Sara Moccia, Danail Stoyanov
FetReg2021: A Challenge on Placental Vessel Segmentation and Registration in Fetoscopy
2022
PDF Bibtex

@article{bano2022fetreg2021,
  title={FetReg2021: A Challenge on Placental Vessel Segmentation and Registration in Fetoscopy},
  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={arXiv preprint arXiv:2206.12512},
  year={2022}
}

Shrawan Kumar Thapa, Pranav Poudel, Binod Bhattarai, Danail Stoyanov
Task-Aware Active Learning for Endoscopic Image Analysis
2022
PDF Bibtex

@article{thapa2022task,
  title={Task-Aware Active Learning for Endoscopic Image Analysis},
  author={Thapa, Shrawan Kumar and Poudel, Pranav and Bhattarai, Binod and Stoyanov, Danail},
  journal={arXiv preprint arXiv:2204.03440},
  year={2022}
}

Binod Bhattarai, Ronast Subedi, Rebati Raman Gaire, Eduard Vazquez, Danail Stoyanov
Histogram of Oriented Gradients Meet Deep Learning: A Novel Multi-task Deep Network for Medical Image Semantic Segmentation
2022
PDF Bibtex

@article{bhattarai2022histogram,
  title={Histogram of Oriented Gradients Meet Deep Learning: A Novel Multi-task Deep Network for Medical Image Semantic Segmentation},
  author={Bhattarai, Binod and Subedi, Ronast and Gaire, Rebati Raman and Vazquez, Eduard and Stoyanov, Danail},
  journal={arXiv preprint arXiv:2204.01712},
  year={2022}
}

Bidur Khanal, Pravin Pokhrel, Bishesh Khanal, Basant Giri
Machine-Learning-Assisted Analysis of Colorimetric Assays on Paper Analytical Devices
ACS Omega, 2021
PDF Bibtex

@article{khanal2021machine,
  title={Machine-Learning-Assisted Analysis of Colorimetric Assays on Paper Analytical Devices},
  author={Khanal, Bidur and Pokhrel, Pravin and Khanal, Bishesh and Giri, Basant},
  journal={ACS omega},
  volume={6},
  number={49},
  pages={33837--33845},
  year={2021},
  publisher={ACS Publications}
}

Liansheng Wang, Cong Xie, Yi Lin, Hong-Yu Zhou, Kailin Chen, Dalong Cheng, Florian Dubost, Benjamin Collery, Bidur Khanal, Bishesh Khanal, Rong Tao, Shangliang Xu, Upasana Upadhyay Bharadwaj, Zhusi Zhong, Jie Li, Shuxin Wang, Shuo Li
Evaluation and comparison of accurate automated spinal curvature estimation algorithms with spinal anterior-posterior X-Ray images: The AASCE2019 challenge
Medical Image Analysis, 2021
PDF Bibtex

@article{wang2021evaluation,
  title={Evaluation and comparison of accurate automated spinal curvature estimation algorithms with spinal anterior-posterior X-Ray images: The AASCE2019 challenge},
  author={Wang, Liansheng and Xie, Cong and Lin, Yi and Zhou, Hong-Yu and Chen, Kailin and Cheng, Dalong and Dubost, Florian and Collery, Benjamin and Khanal, Bidur and Khanal, Bishesh and others},
  journal={Medical Image Analysis},
  volume={72},
  pages={102115},
  year={2021},
  publisher={Elsevier}
}

Chaitanya Kaul, Nick Pears, Suresh Manandhar
FatNet: A feature-attentive network for 3D point cloud processing
IEEE, International Conference on Pattern Recognition (ICPR), 2021
PDF Bibtex

@inproceedings{kaul2021fatnet,
  title={FatNet: A feature-attentive network for 3D point cloud processing},
  author={Kaul, Chaitanya and Pears, Nick and Manandhar, Suresh},
  booktitle={2020 25th International Conference on Pattern Recognition (ICPR)},
  pages={7211--7218},
  year={2021},
  organization={IEEE}
}

Chaitanya Kaul, Nick Pears, Hang Dai, Roderick Murray-Smith, Suresh Manandhar
Penalizing small errors using an Adaptive Logarithmic Loss
International Conference on Pattern Recognition, Springer, Cham, 2021
PDF Bibtex

@inproceedings{kaul2021penalizing,
  title={Penalizing small errors using an adaptive logarithmic loss},
  author={Kaul, Chaitanya and Pears, Nick and Dai, Hang and Murray-Smith, Roderick and Manandhar, Suresh},
  booktitle={International Conference on Pattern Recognition},
  pages={368--375},
  year={2021},
  organization={Springer}
}

Lars Malmqvist, Tommy Yuan, Peter Nightingale, Suresh Manandhar
Determining the Acceptability of Abstract Arguments with Graph Convolutional Networks
[email protected] COMMA, 2020
PDF Bibtex

@inproceedings{malmqvist2020determining,
  title={Determining the Acceptability of Abstract Arguments with Graph Convolutional Networks.},
  author={Malmqvist, Lars and Yuan, Tommy and Nightingale, Peter and Manandhar, Suresh},
  booktitle={[email protected] COMMA},
  pages={47--56},
  year={2020}
}

Lars Malmqvist, Tommy Yuan, Suresh Manandhar
Visualising Argumentation Graphs with Graph Embeddings and t-SNE
2021
PDF Bibtex

@article{malmqvist2021visualising,
  title={Visualising Argumentation Graphs with Graph Embeddings and t-SNE},
  author={Malmqvist, Lars and Yuan, Tommy and Manandhar, Suresh},
  journal={arXiv preprint arXiv:2107.00528},
  year={2021}
}

Sharib Ali, Noha Ghatwary, Debesh Jha, Ece Isik-Polat, Gorkem Polat, Chen Yang, Wuyang Li, Adrian Galdran, Miguel-Ángel González Ballester, Vajira Thambawita, Steven Hicks, Sahadev Poudel, Sang-Woong Lee, Ziyi Jin, Tianyuan Gan, ChengHui Yu, JiangPeng Yan, Doyeob Yeo, Hyunseok Lee, Nikhil Kumar Tomar, Mahmood Haithmi, Amr Ahmed, Michael A. Riegler, Christian Daul, Pål Halvorsen, Jens Rittscher, Osama E. Salem, Dominique Lamarque, Renato Cannizzaro, Stefano Realdon, Thomas de Lange, James E. East
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
2022
PDF Bibtex

@article{ali2022assessing,
  title={Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge},
  author={Ali, Sharib and Ghatwary, Noha and Jha, Debesh and Isik-Polat, Ece and Polat, Gorkem and Yang, Chen and Li, Wuyang and Galdran, Adrian and Ballester, Miguel-{\'A}ngel Gonz{\'a}lez and Thambawita, Vajira and others},
  journal={arXiv preprint arXiv:2202.12031},
  year={2022}
}

Nikhil Kumar Tomar, Debesh Jha, Ulas Bagci, Sharib Ali
TGANet: Text-guided attention for improved polyp segmentation
Medical Image Computing and Computer Assisted Intervention – MICCAI, 2022
PDF Bibtex

@InProceedings{10.1007/978-3-031-16437-8_15,
author="Tomar, Nikhil Kumar
and Jha, Debesh
and Bagci, Ulas
and Ali, Sharib",
editor="Wang, Linwei
and Dou, Qi
and Fletcher, P. Thomas
and Speidel, Stefanie
and Li, Shuo",
title="TGANet: Text-Guided Attention for Improved Polyp Segmentation",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2022",
year="2022",
publisher="Springer Nature Switzerland",
address="Cham",
pages="151--160",
abstract="Colonoscopy is a gold standard procedure but is highly operator-dependent. Automated polyp segmentation, a precancerous precursor, can minimize missed rates and timely treatment of colon cancer at an early stage. Even though there are deep learning methods developed for this task, variability in polyp size can impact model training, thereby limiting it to the size attribute of the majority of samples in the training dataset that may provide sub-optimal results to differently sized polyps. In this work, we exploit size-related and polyp number-related features in the form of text attention during training. We introduce an auxiliary classification task to weight the text-based embedding that allows network to learn additional feature representations that can distinctly adapt to differently sized polyps and can adapt to cases with multiple polyps. Our experimental results demonstrate that these added text embeddings improve the overall performance of the model compared to state-of-the-art segmentation methods. We explore four different datasets and provide insights for size-specific improvements. Our proposed text-guided attention network (TGANet) can generalize well to variable-sized polyps in different datasets. Codes are available at https://github.com/nikhilroxtomar/TGANet.",
isbn="978-3-031-16437-8"
}

Rabin Adhikari, Safal Thapaliya, Nirajan Basnet, Samip Poudel, Aman Shakya, Bishesh Khanal
COVID-19-related Nepali Tweets Classification in a Low Resource Setting
Proceedings of The Seventh Workshop on Social Media Mining for Health Applications (SMM4H), Workshop & Shared Task, COLING 2022, Korea, 2022
PDF Bibtex

@inproceedings{adhikari-etal-2022-covid,
    title = "{COVID}-19-related {N}epali Tweets Classification in a Low Resource Setting",
    author = "Adhikari, Rabin  and
      Thapaliya, Safal  and
      Basnet, Nirajan  and
      Poudel, Samip  and
      Shakya, Aman  and
      Khanal, Bishesh",
    booktitle = "Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.smm4h-1.52",
    pages = "209--215",
}

Sulav Timilsina, Milan Gautam, Binod Bhattarai
NepBERTa: Nepali Language Model Trained in a Large Corpus
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 - AACL-IJCNLP 2022, 2022
PDF Bibtex

@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.",
}