Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation
Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …
clinical approaches. Recent success of deep learning-based segmentation methods usually …
Brain tumor detection and classification using machine learning: a comprehensive survey
J Amin, M Sharif, A Haldorai, M Yasmin… - Complex & intelligent …, 2022 - Springer
Brain tumor occurs owing to uncontrolled and rapid growth of cells. If not treated at an initial
phase, it may lead to death. Despite many significant efforts and promising outcomes in this …
phase, it may lead to death. Despite many significant efforts and promising outcomes in this …
[HTML][HTML] A new mobile application of agricultural pests recognition using deep learning in cloud computing system
ME Karar, F Alsunaydi, S Albusaymi… - Alexandria Engineering …, 2021 - Elsevier
Agricultural pests cause between 20 and 40 percent loss of global crop production every
year as reported by the Food and Agriculture Organization (FAO). Therefore, smart …
year as reported by the Food and Agriculture Organization (FAO). Therefore, smart …
An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review
Background Artificial intelligence (AI) has become a prominent technique for medical
diagnosis and represents an essential role in detecting brain tumors. Although AI-based …
diagnosis and represents an essential role in detecting brain tumors. Although AI-based …
Ambiguity-selective consistency regularization for mean-teacher semi-supervised medical image segmentation
Semi-supervised learning has greatly advanced medical image segmentation since it
effectively alleviates the need of acquiring abundant annotations from experts, wherein the …
effectively alleviates the need of acquiring abundant annotations from experts, wherein the …
Timedistributed-cnn-lstm: A hybrid approach combining cnn and lstm to classify brain tumor on 3d mri scans performing ablation study
Identification of brain tumors at an early stage is crucial in cancer diagnosis, as a timely
diagnosis can increase the chances of survival. Considering the challenges of tumor …
diagnosis can increase the chances of survival. Considering the challenges of tumor …
Caussl: Causality-inspired semi-supervised learning for medical image segmentation
Semi-supervised learning (SSL) has recently demonstrated great success in medical image
segmentation, significantly enhancing data efficiency with limited annotations. However …
segmentation, significantly enhancing data efficiency with limited annotations. However …
Brain tumor diagnosis using machine learning, convolutional neural networks, capsule neural networks and vision transformers, applied to MRI: a survey
Management of brain tumors is based on clinical and radiological information with
presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of …
presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of …
Cascaded deep learning classifiers for computer-aided diagnosis of COVID-19 and pneumonia diseases in X-ray scans
Computer-aided diagnosis (CAD) systems are considered a powerful tool for physicians to
support identification of the novel Coronavirus Disease 2019 (COVID-19) using medical …
support identification of the novel Coronavirus Disease 2019 (COVID-19) using medical …
A deep convolutional neural network based computer aided diagnosis system for the prediction of Alzheimer's disease in MRI images
In the recent past, biomedical domain has become popular due to digital image processing
of accurate and efficient diagnosis of clinical patients using Computer-Aided Diagnosis …
of accurate and efficient diagnosis of clinical patients using Computer-Aided Diagnosis …