@article {ali2021polypgen,
	title = {A multi-center polyp detection and segmentation dataset for generalisability assessment},
	journal = {Nature Scientific Data},
	volume = {10},
	year = {2023},
	publisher = {Nature},
	doi = {https://doi.org/10.1038/s41597-023-01981-y},
	url = {https://doi.org/10.1038/s41597-023-01981-y},
	author = {Ali, Sharib and Jha, Debesh and Ghatwary, Noha and Realdon, Stefano and Cannizzaro, Renato and Osama E {Salem} and Lamarque, Dominique and Daul, Christian and Kim V {Anonsen} and Riegler, Michael and Kim V. {{\r A}nonsen} and Petlund, Andreas and Halvorsen, P{\r a}l and Rittscher, Jens and de Lange, Thomas and James E. {East}}
}
@article {39351,
	title = {FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation},
	journal = {IEEE Transactions on Neural Networks and Learning Systems},
	year = {2022},
	month = {Jan-01-2022},
	pages = {1 - 14},
	publisher = { IEEE},
	abstract = {The increase of available large clinical and experimental datasets has contributed to a substantial amount of important contributions in the area of biomedical image analysis. Image segmentation, which is crucial for any quantitative analysis, has especially attracted attention. Recent hardware advancement has led to the success of deep learning approaches. However, although deep learning models are being trained on large datasets, existing methods do not use the information from different learning epochs effectively. In this work, we leverage the information of each training epoch to prune the prediction maps of the subsequent epochs. We propose a novel architecture called feedback attention network (FANet) that unifies the previous epoch mask with the feature map of the current training epoch. The previous epoch mask is then used to provide hard attention to the learned feature maps at different convolutional layers. The network also allows rectifying the predictions in an iterative fashion during the test time. We show that our proposed feedback attention model provides a substantial improvement on most segmentation metrics tested on seven publicly available biomedical imaging datasets demonstrating the effectiveness of FANet. The source code is available at https://github.com/nikhilroxtomar/FANet.},
	issn = {2162-237X},
	doi = {10.1109/TNNLS.2022.3159394},
	url = {https://ieeexplore.ieee.org/document/9741842},
	author = {Nikhil Kumar {Tomar} and Jha, Debesh and Riegler, Michael and H{\r a}vard D. {Johansen} and Johansen, Dag and Rittscher, Jens and Halvorsen, P{\r a}l and Ali, Sharib}
}
@article {39342,
	title = {Meta-learning with implicit gradients in a few-shot setting for medical image segmentation},
	journal = {Computers in Biology and Medicine},
	volume = {143},
	year = {2022},
	month = {Jan-04-2022},
	pages = {105227},
	publisher = {Elsevier},
	abstract = {Widely used traditional supervised deep learning methods require a large number of training samples but often fail to generalize on unseen datasets. Therefore, a more general application of any trained model is quite limited for medical imaging for clinical practice. Using separately trained models for each unique lesion category or a unique patient population will require sufficiently large curated datasets, which is not practical to use in a real-world clinical set-up. Few-shot learning approaches can not only minimize the need for an enormous number of reliable ground truth labels that are labour-intensive and expensive, but can also be used to model on a dataset coming from a new population. To this end, we propose to exploit an optimization-based implicit model agnostic meta-learning (iMAML) algorithm under few-shot settings for medical image segmentation. Our approach can leverage the learned weights from diverse but small training samples to perform analysis on unseen datasets with high accuracy. We show that, unlike classical few-shot learning approaches, our method improves generalization capability. To our knowledge, this is the first work that exploits iMAML for medical image segmentation and explores the strength of the model on scenarios such as meta-training on unique and mixed instances of lesion datasets. Our quantitative results on publicly available skin and polyp datasets show that the proposed method outperforms the naive supervised baseline model and two recent few-shot segmentation approaches by large margins. In addition, our iMAML approach shows an improvement of 2\%{\textendash}4\% in dice score compared to its counterpart MAML for most experiments.},
	issn = {00104825},
	doi = {10.1016/j.compbiomed.2022.105227},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0010482522000191},
	author = {Khadka, Rabindra and Jha, Debesh and Hicks, Steven and Thambawita, Vajira and Riegler, Michael and Ali, Sharib and Halvorsen, P{\r a}l}
}
@article {39305,
	title = {MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation},
	journal = {IEEE Journal of Biomedical and Health Informatics},
	volume = {26},
	year = {2022},
	month = {12/2021},
	pages = {2252-2263},
	publisher = {IEEE},
	abstract = {Methods based on convolutional neural networks have improved the performance of biomedical image segmentation. However, most of these methods cannot efficiently segment objects of variable sizes and train on small and biased datasets, which are common for biomedical use cases. While methods exist that incorporate multi-scale fusion approaches to address the challenges arising with variable sizes, they usually use complex models that are more suitable for general semantic segmentation problems. In this paper, we propose a novel architecture called Multi-Scale Residual Fusion Network (MSRF-Net), which is specially designed for medical image segmentation. The proposed MSRF-Net is able to exchange multi-scale features of varying receptive fields using a Dual-Scale Dense Fusion (DSDF) block. Our DSDF block can exchange information rigorously across two different resolution scales, and our MSRF sub-network uses multiple DSDF blocks in sequence to perform multi-scale fusion. This allows the preservation of resolution, improved information flow and propagation of both high- and low-level features to obtain accurate segmentation maps. The proposed MSRF-Net allows to capture object variabilities and provides improved results on different biomedical datasets. Extensive experiments on MSRF-Net demonstrate that the proposed method outperforms the cutting edge medical image segmentation methods on four publicly available datasets. We achieve the Dice Coefficient (DSC) of 0.9217, 0.9420, and 0.9224, 0.8824 on Kvasir-SEG, CVC-ClinicDB, 2018 Data Science Bowl dataset, and ISIC-2018 skin lesion segmentation challenge dataset respectively. We further conducted generalizability tests that also achieved the highest DSC score with 0.7921 and 0.7575 on CVC-ClinicDB and Kvasir-SEG, respectively.\&nbsp;},
	issn = {2168-2194},
	doi = {10.1109/JBHI.2021.3138024},
	url = {https://ieeexplore.ieee.org/document/9662196},
	author = {Srivastava, Abhishek and Jha, Debesh and Chanda, Sukalpa and Pal, Umapada and Johansen, Havard and Johansen, Dag and Riegler, Michael and Ali, Sharib and Halvorsen, P{\r a}l}
}
