ConvSegNet: Automated Polyp Segmentation From Colonoscopy Using Context Feature Refinement With Multiple Convolutional Kernel Sizes

Colorectal cancer occurs in the rectal of humans, and early detection has been proved to reduce its mortality rate. Colonoscopy is the standard used in detecting the presence of polyps in the rectal, and accurate segmentation of the polyps from colonoscopy images often provides helpful information for early diagnosis and treatment. Although existing deep learning models often achieve high segmentation performance when tested on the same dataset used in model training; still, their performance often degrades when applied to out-of-distribution datasets, leading to low model generalization or overfitting. This challenge is often associated with the quality of the features learnt from the input images. In this work, a novel Context Feature Refinement (CFR) module is proposed to address the challenge of low model generalization and segmentation performance. The CFR module is built to extract contextual information from the incoming feature map by using multiple parallel convolutional layers with progressively increasing kernel sizes. Using multiple parallel convolutions with different kernel sizes helped to extract more efficient multi-scale contextual information and thus enabled the network to effectively identify and segment small and fine details, as well as larger and more complex structures in the input images. Extensive experiments on three public benchmark datasets in CVC-ClinicDB, Kvasir-SEG, and BKAI-NeoPolyp showed that the proposed ConvSegNet model achieved jaccard, dice and F2 scores of 0.8650, 0.9177, and 0.9328 on CVC-ClinicDB, 0.7936, 0.8618, and 0.8855 on Kvasir-SEG, and 0.8045, 0.8747 and 0.8909 on BKAI-NeoPolyp datasets respectively. Also, an improved generalization performance was achieved by the ConvSegNet model, compared to the benchmark polyp segmentation models. Code is available at https://github.com/AOige/ConvSegNet

Brain volumetric changes and cognitive ageing during the eighth decade of life

Later‐life changes in brain tissue volumes—decreases in the volume of healthy grey and white matter and increases in the volume of white matter hyperintensities (WMH)—are strong candidates to explain some of the variation in ageing‐related cognitive decline. We assessed fluid intelligence, memory, processing speed, and brain volumes (from structural MRI) at mean age 73 years, and at mean age 76 in a narrow‐age sample of older individuals (n = 657 with brain volumetric data at the initial wave, n = 465 at follow‐up). We used latent variable modeling to extract error‐free cognitive levels and slopes. Initial levels of cognitive ability were predictive of subsequent brain tissue volume changes. Initial brain volumes were not predictive of subsequent cognitive changes. Brain volume changes, especially increases in WMH, were associated with declines in each of the cognitive abilities. All statistically significant results were modest in size (absolute r‐values ranged from 0.114 to 0.334). These results build a comprehensive picture of macrostructural brain volume changes and declines in important cognitive faculties during the eighth decade of life

Comparative analysis of university matriculation examination and post university matriculation examination admission models in Lagos State University

There have been issues about the predictive power of the University Matriculation Examination (UME) and most Nigerian universities now conduct an additional screening examination called the post-UME. Some have reported that post-UME is a better predictor of students’ performances than the UME while others have the contrary. Hence, it is still not clear whether post-UME is better than UME. To examine this issue further, the researchers modelled association between entrance exam and academic performance measured by Cumulative Grade Point Average (CGPA) of 381 students who were admitted to eight undergraduate programmes at Lagos State University. Regression analysis showed that UME (standardized β: first year = -0.06, p = 0.214; final year = -0.06, p = 0.217) and General Certificate Ordinary Level, O/L (β: first year = 0.03, p =0.591; final year = 0.02, p = 0.727) were not significantly related to CGPA. However post-UME was significantly associated with CGPA (β: first year = 0.36, p < 0.001; final year = 0.37, p<0.001). Post-UME explained 12.75% and 13.58% variations in the first and final year CGPA respectively. The model that included both post-UME and O/L in the same model showed that they jointly explained 13.07% and 13.81% variations in the first and final year performances respectively Similar results were obtained when UME was added to the model. It was found that post-UME is a better predictor of students’ performances than UME, and the combined O/L and post-UME is no different from the combined O/L, UME and post-UME or post-UME only. The results suggest  that admission criteria should be based largely on post-UME