Attention based multi-scale nested network for biomedical image segmentation.

Convolutional neural network-based methods have significantly enhanced the segmentation performance of biomedical images in recent years. Nevertheless, medical image segmentation presents a challenge marked by layout specificity, with limited variation between samples in medical datasets but significant variation within each individual sample. This aspect has been often overlooked by many models.

GU-Net: Causal relationship-based generative medical image segmentation model.

Due to significant anatomical variations in medical images across different cases, medical image segmentation is a highly challenging task. Convolutional neural networks have shown faster and more accurate performance in medical image segmentation. However, existing networks for medical image segmentation mostly rely on independent training of the model using data samples and loss functions, lacking interactive training and feedback mechanisms.

EA-Net: Research on skin lesion segmentation method based on U-Net.

Accurate segmentation of skin lesions is a challenging task because the task is highly influenced by factors such as location, shape and scale. In recent years, Convolutional Neural Networks (CNNs) have achieved advanced performance in automated medical image segmentation. However, existing CNNs have problems such as inability to highlight relevant features and preserve local features, which limit their application in clinical decision-making.

Brain tumor feature extraction and edge enhancement algorithm based on U-Net network.

Background: Statistics show that each year more than 100,000 patients pass away from brain tumors. Due to the diverse morphology, hazy boundaries, or unbalanced categories of medical data lesions, segmentation prediction of brain tumors has significant challenges.

Purpose: In this thesis, we highlight EAV-UNet, a system designed to accurately detect lesion regions.

Estimation of poverty bounds for Pakistan using synthetic panel data.

Poverty is a big threat to prosperity in developing countries like Pakistan. Alleviating poverty needs concerted efforts including how to measure and analyze poverty. Therefore, this paper employs synthetic panel technique and uses repeated cross-sections household survey dataset (Household Integrated and Economic Survey (HIES)) of Pakistan for 2010-11 and 2015-16, to derive poverty bounds for Pakistan.

Generative adversarial networks with adaptive normalization for synthesizing T2-weighted magnetic resonance images from diffusion-weighted images.

Recently, attention has been drawn toward brain imaging technology in the medical field, among which MRI plays a vital role in clinical diagnosis and lesion analysis of brain diseases. Different sequences of MR images provide more comprehensive information and help doctors to make accurate clinical diagnoses. However, their costs are particularly high.

Self-supervised learning for modal transfer of brain imaging.

Today's brain imaging modality migration techniques are transformed from one modality data in one domain to another. In the specific clinical diagnosis, multiple modal data can be obtained in the same scanning field, and it is more beneficial to synthesize missing modal data by using the diversity characteristics of multiple modal data. Therefore, we introduce a self-supervised learning cycle-consistent generative adversarial network (BSL-GAN) for brain imaging modality transfer.

Comparison of Different Nutritional Screening Approaches and the Determinants of Malnutrition in Under-Five Children in a Marginalized District of Punjab Province, Pakistan.

Objectives: This research measures the occurrence of malnutrition amongst under-five children in the Rahimyar Khan district of Southern Punjab in Pakistan. Employing different anthropometric measurement approaches such as (1) conventional indices (HAZ, WAZ, and WHZ), (2) CIAF, (3) BMI-for-age, and (4) MUAC, we compare their estimated results and examine the relationship between socioeconomic determinants and different anthropometric indicators.

Methods: The study employs a proportional purposive random sampling method to collect data from 384 rural households in the community-based study using a self-administered survey and following the Lady Health Workers (LHWs) registered records.

Distance to Healthcare Facility and Lady Health Workers' Visits Reduce Malnutrition in under Five Children: A Case Study of a Disadvantaged Rural District in Pakistan.

This study accesses the impact of lady health worker (LHWs) visits in the community and distance to a healthcare facility on the nutritional status of under-five children. Additionally, it explores the perceptions and attitudes of the community about the performance of LHWs. A self-administered instrument was applied to gather data on different parameters, such as children's height, age, weight, and socioeconomic status from 384 rural households in a marginalized district of Punjab province with the help of a purposive random sampling technique.