@inproceedings {545,
	title = {Using YOLOv3 to detect colorectal polyps in video},
	booktitle = {Proceedings of the 38th Samahang Pisika ng Pilipinas Physics Conference},
	year = {2020},
	month = {19{\textendash}23 Oct 2020},
	pages = {SPP-2020-2A-05},
	address = {Presented online},
	abstract = {A model based on the You Only Look Once (YOLO) algorithm was trained to detect polyps on a dataset of 503 polyp-positive images taken from colonoscopy video frames. We achieve precision on test sets that is comparable to the performance of a previously described model trained on a much larger set of 5,545 images. However, our model has a lower recall and missed some polyps in the test set.},
	url = {https://paperview.spp-online.org/proceedings/article/view/SPP-2020-2A-05},
	author = {Mary Chris R. Go and Francis N. C. Paraan}
}
@inproceedings {gomcSPP2019,
	title = {Classification of chest radiographs using depthwise separable convolution},
	booktitle = {Proceedings of the 37th Samahang Pisika ng Pilipinas Physics Conference},
	year = {2019},
	month = {29 May 2019},
	pages = {SPP-2019-PA-21},
	address = {Tagbilaran City, Philippines},
	abstract = {Pneumonia x-ray images were classified using a neural network with depthwise separable convolutions. Each image was divided into three vertical regions and convolutions were applied to each region independently. The training model has less training parameters than a standard convolutional neural network (CNN) so that the tendency for overfitting and overall computation time is reduced. The trained network features a relatively high precision (ratio of true and predicted positives) and a significantly shorter training time than a conventional CNN. Our preliminary results indicate that the precision of the neural network was not greatly affected by the introduction of depthwise separable convolutions.},
	url = {https://paperview.spp-online.org/proceedings/article/view/SPP-2019-PA-21},
	author = {Mary Chris R. Go and Francis N. C. Paraan}
}