Quality indicators for colonoscopy in Egypt: A prospective multicenter study.

Background And Study Aims: Despite its wide availability, we do not have sufficient data aboutthe quality of colonoscopy in Egypt. In this study, we proposed 13 indicators to assess the quality of colonoscopy procedures in the included study centers aiming to attain a representative image of the quality of CS in Egypt.

Patients And Methods: A multicenter prospective study was conducted between July and December 2020, which included all patients who underwent colonoscopy in the participating centers.

Compression Helps Deep Learning in Image Classification.

The impact of JPEG compression on deep learning (DL) in image classification is revisited. Given an underlying deep neural network (DNN) pre-trained with pristine ImageNet images, it is demonstrated that, if, for any original image, one can select, among its many JPEG compressed versions including its original version, a suitable version as an input to the underlying DNN, then the classification accuracy of the underlying DNN can be improved significantly while the size in bits of the selected input is, on average, reduced dramatically in comparison with the original image. This is in contrast to the conventional understanding that JPEG compression generally degrades the classification accuracy of DL.