Brain tumor segmentation with corner attention and high-dimensional perceptual loss
Accurate segmentation of brain tumors in MRI sequences is an essential factor that helps
doctors make detailed surgery plans and evaluate prognoses. However, due to the diversity …
doctors make detailed surgery plans and evaluate prognoses. However, due to the diversity …
MDAN: mirror difference aware network for brain stroke lesion segmentation
Brain stroke lesion segmentation is of great importance for stroke rehabilitation
neuroimaging analysis. Due to the large variance of stroke lesion shapes and similarities of …
neuroimaging analysis. Due to the large variance of stroke lesion shapes and similarities of …
PocketNet: A smaller neural network for medical image analysis
A Celaya, JA Actor, R Muthusivarajan… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
Medical imaging deep learning models are often large and complex, requiring specialized
hardware to train and evaluate these models. To address such issues, we propose the …
hardware to train and evaluate these models. To address such issues, we propose the …
A partitioning-stacking prediction fusion network based on an improved attention U-Net for stroke lesion segmentation
H Hui, X Zhang, F Li, X Mei, Y Guo - IEEE Access, 2020 - ieeexplore.ieee.org
Due to the narrow time window for the treatment of acute ischemic stroke, the stroke lesion
area in the patient must be identified as quickly and accurately as possible to evaluate the …
area in the patient must be identified as quickly and accurately as possible to evaluate the …
FTUNet: A Feature-Enhanced Network for Medical Image Segmentation Based on the Combination of U-Shaped Network and Vision Transformer
Semantic Segmentation has been widely used in a variety of clinical images, which greatly
assists medical diagnosis and other work. To address the challenge of reduced semantic …
assists medical diagnosis and other work. To address the challenge of reduced semantic …
Cross-organ, cross-modality transfer learning: feasibility study for segmentation and classification
J Lee, RM Nishikawa - IEEE Access, 2020 - ieeexplore.ieee.org
We conducted two analyses by comparing the transferability of a traditionally transfer-
learned CNN (TL) to that of a CNN fine-tuned with an unrelated set of medical images …
learned CNN (TL) to that of a CNN fine-tuned with an unrelated set of medical images …
Deep learning for neuroimaging segmentation with a novel data augmentation strategy
Brain insults such as cerebral ischemia and intracranial hemorrhage are critical stroke
conditions with high mortality rates. Currently, medical image analysis for critical stroke …
conditions with high mortality rates. Currently, medical image analysis for critical stroke …
R2U3D: Recurrent Residual 3D U-Net for Lung Segmentation
3D Lung segmentation is essential since it processes the volumetric information of the lungs,
removes the unnecessary areas of the scan, and segments the actual area of the lungs in a …
removes the unnecessary areas of the scan, and segments the actual area of the lungs in a …
A novel hybrid convolutional neural network for accurate organ segmentation in 3D head and neck CT images
Radiation therapy (RT) is widely employed in the clinic for the treatment of head and neck
(HaN) cancers. An essential step of RT planning is the accurate segmentation of various …
(HaN) cancers. An essential step of RT planning is the accurate segmentation of various …
A dual-decoding branch U-shaped semantic segmentation network combining transformer attention with decoder: DBUNet
Y Wang, X Yu, X Guo, X Wang, Y Wei, S Zeng - Journal of Visual …, 2023 - Elsevier
Semantic Segmentation is an extremely important medical image auxiliary analysis method.
However, existing networks have the following problems: 1) The amount of feature …
However, existing networks have the following problems: 1) The amount of feature …