Sulav Timilsina, Milan Gautam, Binod Bhattarai
Rabin Adhikari, Safal Thapaliya, Nirajan Basnet, Samip Poudel, Aman Shakya, Bishesh Khanal
Nikhil Kumar Tomar, Debesh Jha, Ulas Bagci, Sharib Ali
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
Pratima Upretee, Bishesh Khanal
Binod Bhattarai, Ronast Subedi, Rebati Raman Gaire, Eduard Vazquez, Danail Stoyanov
Shrawan Kumar Thapa, Pranav Poudel, Binod Bhattarai, Danail Stoyanov
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
Suman Sapkota, Binod Bhattarai
Suman Sapkota, Bidur Khanal, Binod Bhattarai, Bishesh Khanal, Tae-Kyun Kim
Md Kamrul Hasan, Md Ashraful Alam, Lavsen Dahal, Md Toufick E Elahi, Shidhartho Roy, Sifat Redwan Wahid, Robert Marti, Bishesh Khanal
Sharib Ali, Nikhil K Tomar
Lars Malmqvist, Tommy Yuan, Suresh Manandhar
Chaitanya Kaul, Nick Pears, Hang Dai, Roderick Murray-Smith, Suresh Manandhar
Chaitanya Kaul, Nick Pears, Suresh Manandhar
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
Bidur Khanal, Pravin Pokhrel, Bishesh Khanal, Basant Giri
Suman Sapkota, Binod Bhattarai
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
Kiran Raj Pandey, Anup Subedee, Bishesh Khanal, Bhagawan Koirala
E Skelton, J Matthew, Y Li, Bishesh Khanal, JJ Cerrolaza Martinez, N Toussaint, C Gupta, C Knight, B Kainz, JV Hajnal, M Rutherford
Lars Malmqvist, Tommy Yuan, Peter Nightingale, Suresh Manandhar
Shruti Shrestha, Bishesh Khanal, Sharib Ali
Lavsen Dahal, Aayush Kafle, Bishesh Khanal
Alberto Gomez, Veronika A. Zimmer, Bishesh Khanal, Nicolas Toussaint, Julia A. Schnabel
Bidur Khanal, Lavsen Dahal, Prashant Adhikari, Bishesh Khanal
Alberto Gomez, Veronika A. Zimmer, Bishesh Khanal, Nicolas Toussaint, Julia A. Schnabel
Alberto Gomez, Veronika Zimmer, Nicolas Toussaint, Robert Wright, James R. Clough, Bishesh Khanal, Milou Van Poppel, Emily Skelton, Jackie Matthews, and Julia A. Schnabel
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
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
Wright, 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
@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{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}, }
@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{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{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} }
@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{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.", }