Computational Endoscopy, Surgery & Pathology (CESP)

@article{jha2023objective,
  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}
}

@article{subedi2023client,
  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}
}

@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"
}