2024
Pre Print
B Bhattarai MultiModal Learning Lab (MMLL)
Transforming Global health with AI (TOGAI)
2023
Metric Transform: Exploring beyond Affine Transform for Neural Networks
Pre Print
B Bhattarai MultiModal Learning Lab (MMLL)
title={Metric Transform: Exploring beyond Affine Transform for Neural Networks},
author={Sapkota, Suman and Bhattarai, Binod},
year={2023}
}
2023
arXiv preprint arXiv:2304.05339, 2023
Pre Print
Transforming Global health with AI (TOGAI)
title={Deep-learning assisted detection and quantification of (oo) cysts of Giardia and Cryptosporidium on smartphone microscopy images},
author={Nakarmi, Suprim and Pudasaini, Sanam and Thapaliya, Safal and Upretee, Pratima and Shrestha, Retina and Giri, Basant and Neupane, Bhanu Bhakta and Khanal, Bishesh},
journal={arXiv preprint arXiv:2304.05339},
year={2023}
}
2023
arXiv preprint arXiv:2307.16262, 2023
Pre Print
Computational Endoscopy, Surgery & Pathology (CESP)
title={An objective validation of polyp and instrument segmentation methods in colonoscopy through Medico 2020 polyp segmentation and MedAI 2021 transparency challenges},
author={Jha, Debesh and Sharma, Vanshali and Banik, Debapriya and Bhattacharya, Debayan and Roy, Kaushiki and Hicks, Steven A and Tomar, Nikhil Kumar and Thambawita, Vajira and Krenzer, Adrian and Ji, Ge-Peng and others},
journal={arXiv preprint arXiv:2307.16262},
year={2023} }
2023
Simplifying Medical Ultrasound. ASMUS, 2023
Peer Reviewed Workshop article
Transforming Global health with AI (TOGAI)
title={Synthetic Boost: Leveraging Synthetic Data for Enhanced Vision-Language Segmentation in Echocardiography},
author={Adhikari, Rabin and Dhakal, Manish and Thapaliya, Safal and Poudel, Kanchan and Bhandari, Prasiddha and Khanal, Bishesh},
booktitle={International Workshop on Advances in Simplifying Medical Ultrasound},
pages={89--99},
year={2023},
organization={Springer}
}
2023
Exploring transfer learning in medical image segmentation using vision-language models
arXiv preprint arXiv:2308.07706, 2023
Pre-Prints
Transforming Global health with AI (TOGAI)
title={Exploring transfer learning in medical image segmentation using vision-language models},
author={Poudel, Kanchan and Dhakal, Manish and Bhandari, Prasiddha and Adhikari, Rabin and Thapaliya, Safal and Khanal, Bishesh},
journal={arXiv preprint arXiv:2308.07706},
year={2023}
}
2023
arXiv preprint arXiv:2309.12325 , 2023
Pre-Prints
Transforming Global health with AI (TOGAI)
title={FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare},
author={Lekadir, Karim and Feragen, Aasa and Fofanah, Abdul Joseph and Frangi, Alejandro F and Buyx, Alena and Emelie, Anais and Lara, Andrea and Porras, Antonio R and Chan, An-Wen and Navarro, Arcadi and others},
journal={arXiv preprint arXiv:2309.12325},
year={2023}
}
2023
Improving Medical Image Classification in Noisy Labels Using Only Self-supervised Pretraining
MICCAI Workshop on Data Engineering in Medical Imaging. DEMI, 2023
Peer Reviewed Workshop article
Transforming Global health with AI (TOGAI)
B Bhattarai MultiModal Learning Lab (MMLL)
title={Improving Medical Image Classification in Noisy Labels Using only Self-supervised Pretraining},
author={Khanal, Bidur and Bhattarai, Binod and Khanal, Bishesh and Linte, Cristian A},
booktitle={MICCAI Workshop on Data Engineering in Medical Imaging},
pages={78--90},
year={2023},
organization={Springer}
}
2023
Medical Image Analysis, 2023
Peer Reviewed Journal article
B Bhattarai MultiModal Learning Lab (MMLL)
title={CholecTriplet2022: Show me a tool and tell me the triplet--an endoscopic vision challenge for surgical action triplet detection},
author={Nwoye, Chinedu Innocent and Yu, Tong and Sharma, Saurav and Murali, Aditya and Alapatt, Deepak and Vardazaryan, Armine and Yuan, Kun and Hajek, Jonas and Reiter, Wolfgang and Yamlahi, Amine and others},
journal={arXiv preprint arXiv:2302.06294},
year={2023}
}
2023
Emerging Avenue of Artificial Intelligence and Ethical Considerations
arXiv preprint arXiv:2308.07706, 2023
Pre-Prints
Transforming Global health with AI (TOGAI)
title={Emerging Avenue of Artificial Intelligence and Ethical Considerations},
author={Khanal, Bishesh},
year={2023}
}
2023
Neural Network Pruning for Real-time Polyp Segmentation
MIUA, 2023
Peer Reviewed Conference article
B Bhattarai MultiModal Learning Lab (MMLL)
title={Neural Network Pruning for Real-time Polyp Segmentation},
author={Sapkota, Suman and Poudel, Pranav and Regmi, Sudarshan and Panthi, Bibek and Bhattarai, Binod},
journal={arXiv preprint arXiv:2306.13203},
year={2023}
}
2023
arXiv preprint arXiv:2306.12376, 2023
Pre-Prints
Transforming Global health with AI (TOGAI)
title={M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis Tasks},
author={Khanal, Bidur and Bhattarai, Binod and Khanal, Bishesh and Stoyanov, Danail and Linte, Cristian A},
journal={arXiv preprint arXiv:2306.12376},
year={2023}
}
2023
A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentation
Data Engineering in Medical Imaging. DEMI, 2023
Peer Reviewed Workshop article
B Bhattarai MultiModal Learning Lab (MMLL)
Computational Endoscopy, Surgery & Pathology (CESP)
title={A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentation},
author={Subedi, Ronast and Gaire, Rebati Raman and Ali, Sharib and Nguyen, Anh and Stoyanov, Danail and Bhattarai, Binod},
journal={arXiv preprint arXiv:2306.08720},
year={2023}
}
2023
T2FNorm: Extremely Simple Scaled Train-time Feature Normalization for OOD Detection
arXiv preprint arXiv:2305.17797, 2023
Pre-Prints
B Bhattarai MultiModal Learning Lab (MMLL)
title={T2FNorm: Extremely Simple Scaled Train-time Feature Normalization for OOD Detection},
author={Regmi, Sudarshan and Panthi, Bibek and Dotel, Sakar and Gyawali, Prashnna K and Stoynov, Danail and Bhattarai, Binod},
journal={arXiv preprint arXiv:2305.17797},
year={2023}
}
2023
Medical Image Analysis, 2023
Peer Reviewed Journal article
B Bhattarai MultiModal Learning Lab (MMLL)
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}
}
2023
Benchmarking Encoder-Decoder Architectures for Biplanar X-ray to 3D Bone Shape Reconstruction
Neurips, 2023
Peer Reviewed Conference article
Transforming Global health with AI (TOGAI)
title={Challenges of deep learning methods for COVID-19 detection using public datasets},
author={Hasan, Md Kamrul and Alam, Md Ashraful and Dahal, Lavsen and Roy, Shidhartho and Wahid, Sifat Redwan and Elahi, Md Toufick E and Mart{\'\i}, Robert and Khanal, Bishesh}, journal={Informatics in Medicine Unlocked},
volume={30},
pages={100945},
year={2022},
publisher={Elsevier}
}
2023
Investigating the impact of class-dependent label noise in medical image classification
Medical Imaging 2023: Image Processing, vol. 12463, pp. 728-733. SPIE, (2023)
Peer Reviewed Conference article
Transforming Global health with AI (TOGAI)
2023
arXiv preprint arXiv:2206.12512, 2023
Peer Reviewed Journal article
B Bhattarai MultiModal Learning Lab (MMLL)
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}
}
2023
Neural Network , 2023
Pre-Prints
B Bhattarai MultiModal Learning Lab (MMLL)
title={Input Invex Neural Network},
author={Sapkota, Suman and Bhattarai, Binod},
journal={arXiv preprint arXiv:2106.08748},
year={2023}
}
2023
Fast fetal head compounding from multi-view 3D ultrasound
Medical Image Analysis, (2023) : 102793, 2023
Peer Reviewed Journal article
Transforming Global health with AI (TOGAI)
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}
}
2022
Third MICCAI Workshop, DeCaF 2022, and Second MICCAI Workshop, FAIR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022
Peer-reviewed conference articles
Transforming Global health with AI (TOGAI)
title={Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health: Third MICCAI Workshop, DeCaF 2022, and Second MICCAI Workshop, FAIR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022, Proceedings},
author={Albarqouni, Shadi and Bakas, Spyridon and Bano, Sophia and Cardoso, M Jorge and Khanal, Bishesh and Landman, Bennett and Li, Xiaoxiao and Qin, Chen and Rekik, Islem and Rieke, Nicola and others},
volume={13573},
year={2022},
publisher={Springer Nature}
}
2022
Noisy Heuristics NAS: A Network Morphism based Neural Architecture Search using Heuristics
Dynamic Neural Networks, ICML, 2022
Workshop & Challenges
B Bhattarai MultiModal Learning Lab (MMLL)
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}
}
2022
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
Peer-reviewed conference articles
B Bhattarai MultiModal Learning Lab (MMLL)
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."
}
2022
Challenges of Deep Learning Methods for COVID-19 Detection Using Public Datasets
Informatics in Medicine Unlocked 30 (2022): 100945.
Peer Reviewed Journal article
Transforming Global health with AI (TOGAI)
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},
}
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), Korea, 2022
Peer-reviewed workshop articles
Transforming Global health with AI (TOGAI)
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",
}
2022
FixMatchSeg: Fixing FixMatch for Semi-Supervised Semantic Segmentation
2022
Pre-prints
Transforming Global health with AI (TOGAI)
title={FixMatchSeg: Fixing FixMatch for Semi-Supervised Semantic Segmentation},
author={Upretee, Pratima and Khanal, Bishesh},
journal={arXiv preprint arXiv:2208.00400},
year={2022}
}
2022
Task-Aware Active Learning for Endoscopic Polyp Segmentation
arXiv preprint arXiv:2204.03440 , 2022
Pre-prints
B Bhattarai MultiModal Learning Lab (MMLL)
title={Task-Aware Active Learning for Endoscopic Polyp Segmentation},
author={Thapa, Shrawan Kumar and Poudel, Pranav and Regmi, Sudarshan and Bhattarai, Binod and Stoyanov, Danail},
year={2023},
publisher={TechRxiv}
}
2022
Label Geometry Aware Discriminator for Conditional Generative Networks
26th International Conference on Pattern Recognition (ICPR). IEEE. August, 2022
Peer-reviewed conference articles
Transforming Global health with AI (TOGAI)
B Bhattarai MultiModal Learning Lab (MMLL)
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}
}
2022
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
Peer-reviewed conference articles
Computational Endoscopy, Surgery & Pathology (CESP)
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 the 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"
}
2022
Task-Aware Active Learning for Endoscopic Image Analysis
Endoscopic Image Analysis, 2022
Pre-prints
B Bhattarai MultiModal Learning Lab (MMLL)
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}
}
2022
2022
Pre-prints
Computational Endoscopy, Surgery & Pathology (CESP)
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}
}
2021
Machine-Learning-Assisted Analysis of Colorimetric Assays on Paper Analytical Devices
ACS omega 6.49 (2021): 33837-33845., 2021
Peer-reviewed Journal articles
Transforming Global health with AI (TOGAI)
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}
}
2021
Iterative deep learning for improved segmentation of endoscopic images
Nordic Machine Intelligence 1.1 (2021): 38-40., 2021
Workshop & Challenges
Computational Endoscopy, Surgery & Pathology (CESP)
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}
}
2021
Medical Image Analysis 72 (2021): 102115., 2021
Peer-reviewed Journal articles
Transforming Global health with AI (TOGAI)
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}
}
2021
Visualising Argumentation Graphs with Graph Embeddings and t-SNE
2021
Pre-prints
Other Adj. Faculties
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}
}
2021
COVID-19 control strategies and intervention effects in resource limited settings: a modeling study
Plos one 16.6 (2021): e0252570., 2021
Peer-reviewed Journal articles
Transforming Global health with AI (TOGAI)
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}
}
2021
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
Peer-reviewed workshop articles
Other Adj. Faculties
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}
}
2021
FatNet: A feature-attentive network for 3D point cloud processing
25th International conference on pattern recognition (ICPR) (2020). IEEE (2021)., 2021
Peer-reviewed conference articles
Other Adj. Faculties
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}
}
2020
Ensemble U-Net model for efficient polyp segmentation
MediaEval (2020)., 2020
Workshop & Challenges
Transforming Global health with AI (TOGAI)
Computational Endoscopy, Surgery & Pathology (CESP)
title={Ensemble U-Net model for efficient polyp segmentation},
author={Shrestha, Shruti and Khanal, Bishesh and Ali, Sharib},
year={2020}
}
2020
Radiography 27.2 (2021): 519-526., 2020
Peer-reviewed Journal articles
Transforming Global health with AI (TOGAI)
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}
}
2020
Determining the Acceptability of Abstract Arguments with Graph Convolutional Networks
SAFA@ COMMA (2020) (pp. 47-56)., 2020
Peer-reviewed workshop articles
Other Adj. Faculties
title={Determining the Acceptability of Abstract Arguments with Graph Convolutional Networks.},
author={Malmqvist, Lars and Yuan, Tommy and Nightingale, Peter and Manandhar, Suresh},
booktitle={SAFA@ COMMA},
pages={47--56},
year={2020}
}
2020
Uncertainty Estimation in Deep 2D Echocardiography Segmentation
2020
Pre-prints
Transforming Global health with AI (TOGAI)
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}
}
2019
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
Workshop & Challenges
Transforming Global health with AI (TOGAI)
2019
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
Peer-reviewed workshop articles
Transforming Global health with AI (TOGAI)
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}
}
2019
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
Peer-reviewed conference articles
Transforming Global health with AI (TOGAI)
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}
}
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
Peer-reviewed conference articles
Transforming Global health with AI (TOGAI)
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}
}
2019
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
Peer-reviewed conference articles
Transforming Global health with AI (TOGAI)
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}
}
2018
Adapted and Oversegmenting Graphs: Application to Geometric Deep Learning
Pre-prints
Transforming Global health with AI (TOGAI)
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}
}