Publications

@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}
}

@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}
}

@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}
}

                  @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},                    
                  }

@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}
}

@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}
}

@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},
}

@misc{dahal2020uncertainty,
      title={Uncertainty Estimation in Deep 2D Echocardiography Segmentation}, 
      author={Lavsen Dahal and Aayush Kafle and Bishesh Khanal},
      year={2020},
      eprint={2005.09349},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@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},
	
}

@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}
}

@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}
}

@article{shrestha2020ensemble,
  title={Ensemble U-Net model for efficient polyp segmentation},
  author={Shrestha, Shruti and Khanal, Bishesh and Ali, Sharib},
  year={2020}
}

@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}
}

@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}
}

@article{sapkota2021input,
  title={Input Invex Neural Network},
  author={Sapkota, Suman and Bhattarai, Binod},
  journal={arXiv preprint arXiv:2106.08748},
  year={2021}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@article{upretee2022fixmatchseg,
  title={FixMatchSeg: Fixing FixMatch for Semi-Supervised Semantic Segmentation},
  author={Upretee, Pratima and Khanal, Bishesh},
  journal={arXiv preprint arXiv:2208.00400},
  year={2022}
}

@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}
}

@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}
}

@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}
}

@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}
}

@article{ali2021iterative,
  title={Iterative deep learning for improved segmentation of endoscopic images},
  author={Ali, Sharib and Tomar, Nikhil K},
  journal={Nordic Machine Intelligence},
  volume={1},
  number={1},
  pages={38--40},
  year={2021}
}

@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}
}

@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"
}

@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",
}

@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.",
}