Investigating the impact of class-dependent label noise in medical image classification
Medical Imaging 2023: Image Processing, vol. 12463, pp. 728-733. SPIE, (2023), 2023
Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings
Bano, Sophia, et al. "Fetreg2021: a challenge on placental vessel segmentation and registration in fetoscopy.", (2022)., 2023
PDF Bibtex
@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} }
Fast fetal head compounding from multi-view 3D ultrasound
Medical Image Analysis, (2023) : 102793, 2023
Bibtex
@article{wright2023fast, title={Fast fetal head compounding from multi-view 3D ultrasound}, author={Wright, Robert and Gomez, Alberto and Zimmer, Veronika A and Toussaint, Nicolas and Khanal, Bishesh and Matthew, Jacqueline and Skelton, Emily and Kainz, Bernhard and Rueckert, Daniel and Hajnal, Joseph V and others}, journal={Medical Image Analysis}, pages={102793}, year={2023}, publisher={Elsevier} }
Histogram of Oriented Gradients Meet Deep Learning: A Novel Multi-task Deep Network for Medical Image Semantic Segmentation
Medical Image Analysis, 2023
PDF Bibtex
@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} }
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", }
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, (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.", }
TGANet: Text-guided attention for improved polyp segmentation
Medical Image Computing and Computer Assisted Intervention–MICCAI (2022): 25th International Conference, Singapore, (September 18–22, 2022), Proceedings, Part III. Cham: Springer Nature Switzerland, (2022)., 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" }
Noisy Heuristics NAS: A Network Morphism based Neural Architecture Search using Heuristics
Dynamic Neural Networks, ICML, 2022
Label Geometry Aware Discriminator for Conditional Generative Networks
26th International Conference on Pattern Recognition (ICPR). IEEE. August, 2022
PDF Bibtex
@inproceedings{sapkota2022label, title={Label Geometry Aware Discriminator for Conditional Generative Adversarial Networks}, author={Sapkota, Suman and Khanal, Bidur and Bhattarai, Binod and Khanal, Bishesh and Kim, Tae-Kyun}, booktitle={2022 26th International Conference on Pattern Recognition (ICPR)}, pages={2914--2920}, year={2022}, organization={IEEE} }
FixMatchSeg: Fixing FixMatch for Semi-Supervised Semantic Segmentation
2022
Challenges of Deep Learning Methods for COVID-19 Detection Using Public Datasets
Informatics in Medicine Unlocked 30 (2022): 100945., 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}, }
Task-Aware Active Learning for Endoscopic Image Analysis
Endoscopic Image Analysis, 2022
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} }
Input Invex Neural Network
Neural Network , 2021
Machine-Learning-Assisted Analysis of Colorimetric Assays on Paper Analytical Devices
ACS omega 6.49 (2021): 33837-33845., 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} }
Iterative deep learning for improved segmentation of endoscopic images
Nordic Machine Intelligence 1.1 (2021): 38-40., 2021
Evaluation and Comparison of Accurate Automated Spinal Curvature Estimation Algorithms with Spinal Anterior-posterior X-Ray Images: The AASCE2019 Challenge
Medical Image Analysis 72 (2021): 102115., 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} }
Visualising Argumentation Graphs with Graph Embeddings and t-SNE
2021
Theme:Other Adj. Faculties
COVID-19 control strategies and intervention effects in resource limited settings: a modeling study
Plos one 16.6 (2021): e0252570., 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} }
Penalizing small errors using an Adaptive Logarithmic Loss
Pattern Recognition. ICPR International Workshops and Challenges (January 10-15, 2021). Proceedings, Part I. Cham: Springer International Publishing,, 2021
Theme:Other Adj. Faculties
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} }
FatNet: A feature-attentive network for 3D point cloud processing
25th International conference on pattern recognition (ICPR) (2020). IEEE (2021)., 2021
Theme:Other Adj. Faculties
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} }
Towards automated extraction of 2D standard fetal head planes from 3D ultrasound acquisitions: A clinical evaluation and quality assessment comparison
Radiography 27.2 (2021): 519-526., 2020
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}, }
Determining the Acceptability of Abstract Arguments with Graph Convolutional Networks
SAFA@ COMMA (2020) (pp. 47-56)., 2020
Theme:Other Adj. Faculties
Uncertainty Estimation in Deep 2D Echocardiography Segmentation
2020
Ensemble U-Net model for efficient polyp segmentation
MediaEval (2020)., 2020
Automatic Cobb Angle Detection using Vertebra Detector and Vertebra Corners Regression
Computational Methods and Clinical Applications for Spine Imaging: 6th International Workshop and Challenge, CSI (2019), Shenzhen, China, (October 17, 2019), Proceedings 6. Springer International Publishing (2020)., 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 = {International Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging. CSI Workshop and Challenge} }
Image Reconstruction in a Manifold of Image Patches: Application to Whole-fetus Ultrasound Imaging
Machine Learning for Medical Image Reconstruction: Second International Workshop, MLMIR (2019), Held in Conjunction with MICCAI (2019), Shenzhen, China, (October 17, 2019), Proceedings. Cham: Springer International Publishing, (2019)., 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={Machine Learning for Medical Image Reconstruction - MLMIR}, year={2019}, note={Accepted}, }
Towards whole placenta segmentation at late gestation using multi-view ultrasound images
Medical Image Computing and Computer Assisted Intervention–MICCAI (2019): 22nd International Conference, Shenzhen, China, (October 13–17, 2019), Proceedings, Part V 22. Springer International Publishing, (2019)., 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} }
Confident Head Circumference Measurement from Ultrasound with Real-time Feedback for Sonographers
Medical Image Computing and Computer Assisted Intervention–MICCAI (2019): 22nd International Conference, Shenzhen, China, (October 13–17, 2019), Proceedings, Part IV 22. Springer International Publishing, (2019)., 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} }
Complete Fetal Head Compounding from Multi-View 3D Ultrasound
Medical Image Computing and Computer Assisted Intervention–MICCAI (2019): 22nd International Conference, Shenzhen, China, (October 13–17, 2019), Proceedings, Part III 22. Springer International Publishing, (2019)., 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 Matthew, Jacqueline and Skelton, Emily and Khanal, Bishesh and Kainz, Bernhard and Reuckert, Daniel and Hajnal, Jo and Schnabel, Julia}, booktitle={MICCAI}, year={2019}, note={Accepted}, }
Adapted and Oversegmenting Graphs: Application to Geometric Deep Learning
2019