@inproceedings {42722,
	title = {PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps},
	journal = {IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)},
	year = {2022},
	month = {07/2022},
	pages = {66-71},
	publisher = {IEEE},
	abstract = {Early identification of a polyp in the lower gastrointestinal (GI) tract can lead to prevention of life-threatening colorectal cancer. Developing computer-aided diagnosis (CAD) systems to detect polyps can improve detection accuracy and efficiency and save the time of the domain experts called endoscopists. Lack of annotated data is a common challenge when building CAD systems. Generating synthetic medical data is an active research area to overcome the problem of having relatively few true positive cases in the medical domain. To be able to efficiently train machine learning (ML) models, which are the core of CAD systems, a considerable amount of data should be used. In this respect, we propose the PolypConnect pipeline, which can convert non-polyp images into polyp images to increase the size of training datasets for training. We present the whole pipeline with quantitative and qualitative evaluations involving endoscopists. The polyp segmentation model trained using synthetic data, and real data shows a 5.1\% improvement of mean intersection over union (mIOU), compared to the model trained only using real data. The codes of all the experiments are available on GitHub to reproduce the results.},
	doi = {10.1109/CBMS55023.2022.00019},
	author = {Jan Andre {Fageren} and Thambawita, Vajira and Stor{\r a}s, Andrea and Parasa, Sravanthi and de Lange, Thomas and Halvorsen, P{\r a}l and Riegler, Michael}
}
@article {42539,
	title = {SinGAN-Seg: Synthetic training data generation for medical image segmentation},
	journal = {PLOS ONE},
	volume = {17},
	year = {2022},
	month = {05/2022},
	pages = {e0267976},
	publisher = {PLOS},
	abstract = {Analyzing medical data to find abnormalities is a time-consuming and costly task, particularly for rare abnormalities, requiring tremendous efforts from medical experts. Therefore, artificial intelligence has become a popular tool for the automatic processing of medical data, acting as a supportive tool for doctors. However, the machine learning models used to build these tools are highly dependent on the data used to train them. Large amounts of data can be difficult to obtain in medicine due to privacy reasons, expensive and time-consuming annotations, and a general lack of data samples for infrequent lesions. In this study, we present a novel synthetic data generation pipeline, called\&nbsp;SinGAN-Seg, to produce synthetic medical images with corresponding masks using a single training image. Our method is different from the traditional generative adversarial networks (GANs) because our model needs only a single image and the corresponding ground truth to train. We also show that the synthetic data generation pipeline can be used to produce alternative artificial segmentation datasets with corresponding ground truth masks when real datasets are not allowed to share. The pipeline is evaluated using qualitative and quantitative comparisons between real data and synthetic data to show that the style transfer technique used in our pipeline significantly improves the quality of the generated data and our method is better than other state-of-the-art GANs to prepare synthetic images when the size of training datasets are limited. By training UNet++ using both real data and the synthetic data generated from the SinGAN-Seg pipeline, we show that the models trained on synthetic data have very close performances to those trained on real data when both datasets have a considerable amount of training data. In contrast, we show that synthetic data generated from the SinGAN-Seg pipeline improves the performance of segmentation models when training datasets do not have a considerable amount of data. All experiments were performed using an open dataset and the code is publicly available on GitHub.},
	doi = {https://doi.org/10.1371/journal.pone.0267976},
	url = {https://doi.org/10.1371/journal.pone.0267976},
	author = {Thambawita, Vajira and Salehi, Pegah and Sajad Amouei {Sheshkal} and Hicks, Steven and Hugo Lewi {Hammer} and Parasa, Sravanthi and de Lange, Thomas and Halvorsen, P{\r a}l and Riegler, Michael}
}
@misc {42764,
	title = {Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations},
	year = {2022},
	publisher = {arXiv},
	doi = {10.48550/arXiv.2204.00617},
	url = {https://arxiv.org/abs/2204.00617},
	author = {Hicks, Steven and Stor{\r a}s, Andrea and Riegler, Michael and Midoglu, Cise and Hammou, Malek and de Lange, Thomas and Parasa, Sravanthi and Halvorsen, P{\r a}l and Str{\"u}mke, Inga}
}
@inproceedings {27831,
	title = {Data Augmentation Using Generative Adversarial Networks For Creating Realistic Artificial Colon Polyp Images},
	journal = {DDW 2021},
	year = {2021},
	author = {Thambawita, Vajira and Str{\"u}mke, Inga and Hicks, Steven and Riegler, Michael and Halvorsen, P{\r a}l and Parasa, Sravanthi}
}
@inproceedings {27834,
	title = {DeepSynthBody: the beginning of the end for data deficiency in medicine},
	journal = {The International Conference on Applied Artificial Intelligence (ICAPAI)},
	year = {2021},
	publisher = {IEEE},
	abstract = {Limited access to medical data is a barrier on developing new and efficient machine learning solutions in medicine such as computer-aided diagnosis, risk assessments, predicting optimal treatments and home-based personal healthcare systems. This paper presents DeepSynthBody: a novel framework that overcomes some of the inherent restrictions and limitations of medical data by using deep generative adversarial networks to produce synthetic data with characteristics similar to the real data, so-called DeepSynth (deep synthetic) data. We show that DeepSynthBody can address two key issues commonly associated with medical data, namely privacy concerns (as a result of data protection rules and regulations) and the high costs of annotations. To demonstrate the full pipeline of applying DeepSynthBody concepts and user-friendly functionalities, we also describe a synthetic medical dataset generated and published using our framework. DeepSynthBody opens a new era of machine learning applications in medicine with a synthetic model of the human body.},
	doi = {10.1109/ICAPAI49758.2021.9462062},
	author = {Thambawita, Vajira and Hicks, Steven and Isaksen, Jonas and Mette Haug {Stensen} and Trine B {Haugen} and Kanters, J{\o}rgen and Parasa, Sravanthi and de Lange, Thomas and H{\r a}vard D. {Johansen} and Johansen, Dag and Hugo Lewi {Hammer} and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {27832,
	title = {Impact of Image Resolution on Convolutional Neural Networks Performance in Gastrointestinal Endoscopy},
	journal = {DDW 2021},
	year = {2021},
	abstract = {Convolutional neural networks (CNNs) are increasingly used to improve and automate processes in gastroenterology, like the detection of polyps during a colonoscopy. An important input to these methods is images and videos. Up until now, no well-defined, common understanding or standard regarding the resolution of the images and video frames has been defined, and to reduce processing time and resource requirements, images are today almost always down-sampled. However, how such down-sampling and the image resolution influence the performance in context with medical data is unknown. In this work, we investigate how the resolution relates to the performance of convolutional neural networks. This can help set standards for image or video characteristics for future CNN based models in gastrointestinal endoscopy.},
	author = {Thambawita, Vajira and Hicks, Steven and Str{\"u}mke, Inga and Riegler, Michael and Halvorsen, P{\r a}l and Parasa, Sravanthi}
}
@article {28215,
	title = {Impact of Image Resolution on Deep Learning Performance in Endoscopy Image Classification: An Experimental Study Using a Large Dataset of Endoscopic Images},
	journal = {Diagnostics},
	volume = {11},
	year = {2021},
	month = {09/2021},
	publisher = {MDPI},
	abstract = {Recent trials have evaluated the efficacy of deep convolutional neural network (CNN)-based AI systems to improve lesion detection and characterization in endoscopy. Impressive results are achieved, but many medical studies use a very small image resolution to save computing resources at the cost of losing details. Today, no conventions between resolution and performance exist, and monitoring the performance of various CNN architectures as a function of image resolution provides insights into how subtleties of different lesions on endoscopy affect performance. This can help set standards for image or video characteristics for future CNN-based models in gastrointestinal (GI) endoscopy. This study examines the performance of CNNs on the HyperKvasir dataset, consisting of 10,662 images from 23 different findings. We evaluate two CNN models for endoscopic image classification under quality distortions with image resolutions ranging from 32 {\texttimes} 32 to 512 {\texttimes} 512 pixels. The performance is evaluated using two-fold cross-validation and F1-score, maximum Matthews correlation coefficient (MCC), precision, and sensitivity as metrics. Increased performance was observed with higher image resolution for all findings in the dataset. MCC was achieved at image resolutions between 512 {\texttimes} 512 pixels for classification for the entire dataset after including all subclasses. The highest performance was observed with an MCC value of 0.9002 when the models were trained on the highest resolution and tested on the same resolution. Different resolutions and their effect on CNNs are explored. We show that image resolution has a clear influence on the performance which calls for standards in the field in the future.},
	doi = {https://doi.org/10.3390/diagnostics11122183},
	url = {https://www.mdpi.com/2075-4418/11/12/2183},
	author = {Thambawita, Vajira and Str{\"u}mke, Inga and Hicks, Steven and Halvorsen, P{\r a}l and Parasa, Sravanthi and Riegler, Michael}
}
@inproceedings {40625,
	title = {Medico Multimedia Task at MediaEval 2021: Transparency in Medical Image Segmentation},
	journal = {Mediaeval Medico 2021},
	year = {2021},
	publisher = {Mediaeval 2021},
	abstract = {The Medico Multimedia Task\&nbsp;focuses on providing multimedia researchers with the opportunity to contribute to different areas of medicine using multimedia data to solve several subtasks. This year, the task focuses on transparency within machine learning-based medical segmentation systems, where the use case is gastrointestinal endoscopy. In this paper, we motivate the organization of this task, describe the development and test dataset, and present the evaluation process used to assess the participants{\textquoteright} submissions.},
	url = {https://2021.multimediaeval.com/},
	author = {Hicks, Steven and Jha, Debesh and Thambawita, Vajira and Hugo Lewi {Hammer} and de Lange, Thomas and Parasa, Sravanthi and Riegler, Michael and Halvorsen, P{\r a}l}
}
