Deep learning for smart Healthcare—A survey on brain tumor detection from medical imaging
M Arabahmadi, R Farahbakhsh, J Rezazadeh - Sensors, 2022 - mdpi.com
Advances in technology have been able to affect all aspects of human life. For example, the
use of technology in medicine has made significant contributions to human society. In this …
use of technology in medicine has made significant contributions to human society. In this …
Deep transfer learning approaches in performance analysis of brain tumor classification using MRI images
Brain tumor classification is a very important and the most prominent step for assessing life‐
threatening abnormal tissues and providing an efficient treatment in patient recovery. To …
threatening abnormal tissues and providing an efficient treatment in patient recovery. To …
Fully convolutional network for the semantic segmentation of medical images: A survey
SY Huang, WL Hsu, RJ Hsu, DW Liu - Diagnostics, 2022 - mdpi.com
There have been major developments in deep learning in computer vision since the 2010s.
Deep learning has contributed to a wealth of data in medical image processing, and …
Deep learning has contributed to a wealth of data in medical image processing, and …
Association of brain atrophy with disease progression independent of relapse activity in patients with relapsing multiple sclerosis
A Cagol, S Schaedelin, M Barakovic, P Benkert… - JAMA …, 2022 - jamanetwork.com
Importance The mechanisms driving neurodegeneration and brain atrophy in relapsing
multiple sclerosis (RMS) are not completely understood. Objective To determine whether …
multiple sclerosis (RMS) are not completely understood. Objective To determine whether …
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 …
Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels
Background Accurate segmentation of brain tumour in magnetic resonance images (MRI) is
a difficult task due to various tumour types. Using information and features from multimodal …
a difficult task due to various tumour types. Using information and features from multimodal …
ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI
Performance of models highly depend not only on the used algorithm but also the data set it
was applied to. This makes the comparison of newly developed tools to previously …
was applied to. This makes the comparison of newly developed tools to previously …
Neurofilament light chain elevation and disability progression in multiple sclerosis
A Abdelhak, P Benkert, S Schaedelin… - JAMA …, 2023 - jamanetwork.com
Importance Mechanisms contributing to disability accumulation in multiple sclerosis (MS) are
poorly understood. Blood neurofilament light chain (NfL) level, a marker of neuroaxonal …
poorly understood. Blood neurofilament light chain (NfL) level, a marker of neuroaxonal …
Predicting infarct core from computed tomography perfusion in acute ischemia with machine learning: Lessons from the ISLES challenge
Background and Purpose: The ISLES challenge (Ischemic Stroke Lesion Segmentation)
enables globally diverse teams to compete to develop advanced tools for stroke lesion …
enables globally diverse teams to compete to develop advanced tools for stroke lesion …
FedMed-GAN: Federated domain translation on unsupervised cross-modality brain image synthesis
Utilizing multi-modal neuroimaging data is proven to be effective in investigating human
cognitive activities and certain pathologies. However, it is not practical to obtain the full set of …
cognitive activities and certain pathologies. However, it is not practical to obtain the full set of …