Brain tumor analysis using deep learning and VGG-16 ensembling learning approaches
A brain tumor is a distorted tissue wherein cells replicate rapidly and indefinitely, with no
control over tumor growth. Deep learning has been argued to have the potential to …
control over tumor growth. Deep learning has been argued to have the potential to …
Recent deep learning-based brain tumor segmentation models using multi-modality magnetic resonance imaging: a prospective survey
Radiologists encounter significant challenges when segmenting and determining brain
tumors in patients because this information assists in treatment planning. The utilization of …
tumors in patients because this information assists in treatment planning. The utilization of …
Comprehensive Review on MRI-Based Brain Tumor Segmentation: A Comparative Study from 2017 Onwards
Brain tumor segmentation has been a challenging and popular research problem in the area
of medical imaging and computer-aided diagnosis. In the last few years, especially since …
of medical imaging and computer-aided diagnosis. In the last few years, especially since …
Style transfer in conditional GANs for cross-modality synthesis of brain magnetic resonance images
Z Qin, Z Liu, P Zhu, W Ling - Computers in Biology and Medicine, 2022 - Elsevier
Magnetic resonance imaging (MRI) has become one of the most standardized and widely
used neuroimaging protocols in the detection and diagnosis of neurodegenerative diseases …
used neuroimaging protocols in the detection and diagnosis of neurodegenerative diseases …
YoloCurvSeg: You only label one noisy skeleton for vessel-style curvilinear structure segmentation
Weakly-supervised learning (WSL) has been proposed to alleviate the conflict between data
annotation cost and model performance through employing sparsely-grained (ie, point-, box …
annotation cost and model performance through employing sparsely-grained (ie, point-, box …
Handling missing MRI data in brain tumors classification tasks: Usage of synthetic images vs. duplicate images and empty images
YH Moshe, Y Buchsweiler, M Teicher… - Journal of Magnetic …, 2024 - Wiley Online Library
Background Deep‐learning is widely used for lesion classification. However, in the clinic
patient data often has missing images. Purpose To evaluate the use of generated, duplicate …
patient data often has missing images. Purpose To evaluate the use of generated, duplicate …
Incomplete multimodal learning for visual acuity prediction after cataract surgery using masked self-attention
As the primary treatment option for cataracts, it is estimated that millions of cataract surgeries
are performed each year globally. Predicting the Best Corrected Visual Acuity (BCVA) in …
are performed each year globally. Predicting the Best Corrected Visual Acuity (BCVA) in …
Generative learning-based lightweight MRI brain tumor segmentation with missing modalities
Accurate segmentation of brain tumors across multiple MRI modalities is crucial for clinical
diagnosis and prognosis. However, encountering missing modalities due to practical factors …
diagnosis and prognosis. However, encountering missing modalities due to practical factors …
DS-Net: Difficulty-Perceived Common-to-T1ce Semi-supervised Multimodal MRI Synthesis Network
Abstract Contrast-enhanced T1 (T1ce) is one of the most essential magnetic resonance
imaging (MRI) modalities for diagnosing and analyzing brain tumors, especially gliomas. In …
imaging (MRI) modalities for diagnosing and analyzing brain tumors, especially gliomas. In …
Uncertainty-aware incomplete multimodal fusion for few-shot Central Retinal Artery Occlusion classification
Abstract Central Retinal Artery Occlusion (CRAO) is a rare and severe ophthalmic disease
that remains challenging to accurately diagnose and classify in clinical practice. The low …
that remains challenging to accurately diagnose and classify in clinical practice. The low …