Colorectal cancer is one of the world leading death causes. Fortunately, an early diagnosis allows for effective treatment, increasing the survival rate. Deep learning techniques have shown their utility for increasing the adenoma detection rate at colonoscopy, but a dataset is usually required so the model can automatically learn features that characterize the polyps. In this work, we present the PICCOLO dataset, that comprises 3433 manually annotated images (2131 white-light images 1302 narrow-band images), originated from 76 lesions from 40 patients, which are distributed into training (2203), validation (897) and test (333) sets assuring patient independence between sets. Furthermore, clinical metadata are also provided for each lesion. Four different models, obtained by combining two backbones and two encoder–decoder architectures, are trained with the PICCOLO dataset and other two publicly available datasets for comparison. Results are provided for the test set of each dataset. Models trained with the PICCOLO dataset have a better generalization capacity, as they perform more uniformly along test sets of all datasets, rather than obtaining the best results for its own test set. This dataset is available at the website of the Basque Biobank, so it is expected that it will contribute to the further development of deep learning methods for polyp detection, localisation and classification, which would eventually result in a better and earlier diagnosis of colorectal cancer, hence improving patient outcomes.
The upcoming challenge for higher education institutions (HEIs) globally is how to respond to an increasing variety of societal needs but with fewer public resources and increased accountability demands. In this chapter we draw attention to the central role institutional research (IR) professionals play in positioning HEIs in a competitive and globalized environment, and the need for them to have a well‐developed set of skills (both generic and specialized) to provide and inform the decision‐making process. We conclude by posing some questions to consider for the practice of IR into the future.
Ultrasonography (US) is currently the reference technique for evaluating gallbladder pathology. The aim of this study was to prospectively determine the diagnostic efficacy of magnetic resonance cholangiography (MRCP) in evaluating the gallbladder, as compared with US. The study included 80 patients (mean age, 69.3 years; male-to-female ration, 1.3:1) who underwent prospective US and MRCP; 5 patients in whom MRCP was contraindicated were excluded. In all cases, US was performed before MRCP. Ultrasound was the reference technique for evaluating MRCP sensitivity and specificity. Magnetic resonance cholangiopancreatography provided good image quality in 65 patients (81.2%) and poor image quality in 15 (mostly because of poor patient cooperation). Artefacts did not influence visualization of the gallbladder or evaluation of the background pathology. The sensitivity of MRCP in diagnosing gallbladder stones (43 patients; 97.7%) was comparable to US (44 patients). In contrast, MRCP diagnosed biliary sludge or microlithiasis in 13 patients, versus 5 in the case of US. Magnetic resonance cholangiopancreatography is a good technique for diagnosing cholelithiasis and biliary sludge. However, its high cost, contraindications, and the need for patient cooperation limit the use of the technique in routine clinical gallbladder studies. Magnetic resonance cholangiopancreatography could contribute to the diagnosis of microlithiasis, provided that future studies confirm its greater sensitivity versus US.
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