@inproceedings {26207,
	title = {Deep Learning Based Disease Detection Using Domain Specific Transfer Learning},
	journal = {MediaEval 2018},
	year = {2018},
	publisher = {MediaEval},
	abstract = {In this paper, we present our approach for the Medico Multimedia\&nbsp;Task as part of the MediaEval 2018 Benchmark. Our method is based on convolutional neural networks (CNNs), where we compare how fine-tuning, in the context of transfer learning, from different source domains (general versus medical domain) affect classification\&nbsp;performance. The preliminary results show that fine-tuning models trained on large and diverse datasets is favorable, even when the model{\textquoteright}s source domain has little to no resemblance to the new target.\&nbsp;},
	keywords = {convolutional neural networks, deep learning, Gastrointestinal Disease Detection},
	author = {Hicks, Steven and Pia H. {Smedsrud} and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {26205,
	title = {The Medico-Task 2018: Disease Detection in the Gastrointestinal Tract using Global Features and Deep Learning},
	journal = {MediaEval 2018},
	year = {2018},
	month = {10/2018},
	publisher = {MediaEval},
	address = {Nice, France},
	abstract = {In this paper, we present our approach for the 2018 Medico Task classifying diseases in the gastrointestinal tract. We have proposed a system based on global features and deep neural networks. The best approach combines two neural networks and the reproducible experimental results signify the efficiency of the proposed model with an accuracy rate of 95.80\%, a precision of 95.87\%, and an F1-score of 95.80\%.},
	keywords = {CNN, deep learning, Gastrointestinal Disease Detection, Global Features, Medico-Task 2018, Transfer Learning},
	author = {Thambawita, Vajira and Jha, Debesh and Riegler, Michael and Halvorsen, P{\r a}l and Hugo Lewi {Hammer} and H{\r a}vard D. {Johansen} and Johansen, Dag}
}
