@inproceedings {39516,
	title = {GMSRF-Net: An Improved generalizability with Global Multi-Scale Residual Fusion Network for Polyp Segmentation},
	journal = {26th International Conference on Pattern Recognition},
	year = {2022},
	publisher = { IEEE},
	abstract = {Colonoscopy is a gold standard procedure but is highly operator-dependent. Efforts have been made to automate the detection and segmentation of polyps, a precancerous precursor, to effectively minimize missed rate. Widely used computer-aided polyp segmentation systems actuated by encoder-decoder have achieved high performance in terms of accuracy. However, polyp segmentation datasets collected from varied centers can follow different imaging protocols leading to difference in data distribution. As a result, most methods suffer from performance drop when trained and tested on different distributions and therefore, require re-training for each specific dataset. We address this generalizability issue by proposing a global multi-scale residual fusion network (GMSRF-Net). Our proposed network maintains high-resolution representations by performing multi-scale fusion operations across all resolution scales through dense connections while preserving low-level information. To further leverage scale information, we design cross multi-scale attention (CMSA) module that uses multi-scale features to identify, keep, and propagate informative features. Additionally, we introduce multi-scale feature selection (MSFS) modules to perform channel-wise attention that gates irrelevant features gathered through global multi-scale fusion within the GMSRF-Net. The repeated fusion operations gated by CMSA and MSFS demonstrate improved generalizability of our network.\&nbsp;Experiments conducted on two different polyp segmentation datasets show that our proposed GMSRF-Net outperforms the previous top-performing state-of-the-art method by 8.34\% and 10.31\% on unseen CVC-ClinicDB and on unseen Kvasir-SEG, in terms of dice coefficient. Additionally, when tested on unseen CVC-ColonDB, we surpass the state-of-the-art method by 9.38\% and 4.04\% in terms of dice coefficient, when source dataset is Kvasir-SEG and CVC-ClinicDB, respectively.},
	doi = {10.1109/ICPR56361.2022.9956726},
	author = {Srivastava, Abhishek and Chanda, Sukalpa and Jha, Debesh and Pal, Umapada and Ali, Sharib}
}
@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}
}
@inproceedings {39299,
	title = {PAANet: Progressive Alternating Attention for Automatic Medical Image Segmentation},
	journal = {Proc. of 2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)},
	year = {2021},
	month = {12/2021},
	publisher = {IEEE},
	address = {Paris / Cr{\'e}teil, France},
	abstract = {Medical image segmentation can provide detailed information for clinical analysis which can be useful for scenarios where the detailed location of a finding is important. Knowing the location of a disease can play a vital role in treatment and decision-making. Convolutional neural network (CNN) based encoder-decoder techniques have advanced the performance of automated medical image segmentation systems. Several such CNN-based methodologies utilize techniques such as spatial- and channel-wise attention to enhance performance. Another technique that has drawn attention in recent years is residual dense blocks (RDBs). The successive convolutional layers in densely connected blocks are capable of extracting diverse features with varied receptive fields and thus, enhancing performance. However, consecutive stacked convolutional operators may not necessarily generate features that facilitate the identification of the target structures. In this paper, we propose a progressive alternating attention network (PAANet). We develop progressive alternating attention dense (PAAD) blocks, which construct a guiding attention map (GAM) after every convolutional layer in the dense blocks using features from all scales. The GAM allows the following layers in the dense blocks to focus on the spatial locations relevant to the target region. Every alternatePAAD block inverts the GAM to generate a reverse attention map which guides ensuing layers to extract boundary and edge-related information, refining the segmentation process. Our experiments on three different biomedical image segmentation datasets exhibit that our PAANet achieves favorable performance when compared to other state-of-the-art methods.\&nbsp;},
	doi = {10.1109/BioSMART54244.2021.9677844},
	url = {https://ieeexplore.ieee.org/document/9677844},
	author = {Srivastava, Abhishek and Chanda, Sukalpa and Jha, Debesh and Riegler, Michael and Halvorsen, P{\r a}l and Johansen, Dag and Pal, Umapada}
}
