Titlebook: Computer-Aided Analysis of Gastrointestinal Videos; Jorge Bernal,Aymeric Histace Book 2021 Springer Nature Switzerland AG 2021 Computer Vi

MIRE

multiply

Convolutional LSTMysis (Wang et?al. .; Urban et?al. .; Shin et?al. .; Mohammed et?al. .). Colonoscopy, however, is a video-based modality and an endoscopist will always use the contextual information from previous frames to make an accurate decision about the potential presence of a polyp. Recent developments in sema

Toxoid-Vaccines

Book 2021formance obtained by the 20 participating teams. The early and accurate diagnosis of gastrointestinal diseases is critical for increasing the chances of patient survival, and efficient screening is vital for locating precursor lesions. Video colonoscopy and wireless capsule endoscopy (WCE) are the g

cartilage

轎車

Multi-scale Ensemble of ResNet Variants to win many AI competitions. These methods are especially effective when the models are diverse?(Brown et?al. .). We achieve this diversity by using different ResNet models and by employing the multi-scale approach.

Adulate

https://doi.org/10.1057/9781137443946ation of pixels, with a Markov Random Field property to improve the neighborhood of the lesion. This was done with the CIELab color space, since it was found that has high efficiency in differentiating colors in an image.

反感

backdrop

https://doi.org/10.1007/978-3-030-91363-2ence value. The anchors have different aspect ratios and scales. The classifier network crops these anchors from the feature maps of the last convolutional layer and feeds the cropped features to the remainder of the network in order to predict location and confidence values of the object class (polyps).

逃避現(xiàn)實(shí)

Combination of Color-Based Segmentation, Markov Random Fields and Multilayer Perceptronation of pixels, with a Markov Random Field property to improve the neighborhood of the lesion. This was done with the CIELab color space, since it was found that has high efficiency in differentiating colors in an image.