@inproceedings {39534,
	title = {Kvasir-instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy},
	journal = {International Conference on Multimedia Modeling},
	year = {2020},
	publisher = { Springer},
	abstract = {Gastrointestinal\&nbsp;pathologies are periodically screened, biopsied, and resected using surgical tools. Usually, the procedures and the treated or resected areas are not specifically tracked or analysed during or after colonoscopies. Information regarding disease borders, development,\&nbsp; amount, and size of the resected area get lost. This can lead to poor follow-up and bothersome reassessment difficulties post-treatment. To improve the current standard and also to foster more research on the topic, we have released the\&nbsp; {\textquoteleft}{\textquoteleft}Kvasir-Instrument{\textquoteright}{\textquoteright} dataset, which consists of 590\&nbsp;annotated frames containing GI\&nbsp;procedure tools such as snares, balloons, and biopsy forceps, etc. Besides the images, the dataset includes ground truth masks and bounding boxes and has been verified by two expert GI\&nbsp;endoscopists. Additionally, we provide a baseline for the segmentation of the GI tools to promote research and algorithm development. We obtained a dice coefficient score of 0.9158\&nbsp;and a Jaccard index of 0.8578\&nbsp;using a classical U-Net architecture. A similar dice coefficient score was observed for DoubleUNet. The qualitative results showed that the model did not work for the images with specularity and the frames with multiple tools, while the best result for both methods was observed on all other types of images. Both qualitative and quantitative results show that the model performs reasonably good, but there is potential for further improvements.\&nbsp; Benchmarking using the dataset provides an opportunity for researchers to contribute to the field of automatic endoscopic diagnostic and therapeutic tool segmentation for GI\&nbsp;endoscopy.},
	keywords = {Benchmarking, Convolutional  neural network, Gastrointestinal endoscopy, Tool segmentation},
	url = {https://www.springerprofessional.de/en/kvasir-instrument-diagnostic-and-therapeutic-tool-segmentation-d/18788368},
	author = {Jha, Debesh and Ali, Sharib and Emanuelsen, Krister and Hicks, Steven and Thambawita, Vajira and Garcia-Ceja, Enrique and Riegler, Michael and de Lange, Thomas and Peter T. {Schmidt} and H{\r a}vard D {Johansen} and Johansen, Dag and Halvorsen, P{\r a}l}
}
@inproceedings {27639,
	title = {LightLayers: Parameter Efficient Dense and Convolutional Layers for Image Classification},
	journal = {The Joint International Conference PDCAT-PAAP 2020},
	year = {2020},
	publisher = { Springer},
	abstract = {Deep Neural Networks (DNNs) have become the de-facto standard in computer vision, as well as in many other pattern recognition tasks. A key drawback of DNNs is that the training phase can be very computationally expensive. Organizations or individuals that cannot afford purchasing state-of-the-art hardware or tapping into cloud-hosted infrastructures may face a long waiting time before the training completes or might not be able to train a model at all. Investigating novel ways to reduce the training time could be a potential solution to alleviate this drawback, and thus enabling more rapid development of new algorithms and models. In this paper, we propose LightLayers, a method for reducing the number of trainable parameters in deep neural networks (DNN). The proposed LightLayers consists of LightDense andLightConv2D layer that are as efficient as regular Conv2D and Dense layers, but uses less parameters. We resort to Matrix Factorization to reduce the complexity of the DNN models resulting into lightweight DNNmodels that require less computational power, without much loss in the accuracy. We have tested LightLayers on MNIST, Fashion MNIST, CI-FAR 10, and CIFAR 100 datasets. Promising results are obtained for MNIST, Fashion MNIST, CIFAR-10 datasets whereas CIFAR 100 shows acceptable performance by using fewer parameters.},
	keywords = {CIFAR-10, Convolutional  neural network, Deep  Learning, Fashion MNIST, Lightweight  model, MNIST, Weight decomposition},
	author = {Jha, Debesh and Yazidi, Anis and Riegler, Michael and Johansen, Dag and H{\r a}vard D. {Johansen} and Halvorsen, P{\r a}l}
}
