[HTML][HTML] A novel hybrid approach based on deep CNN to detect glaucoma using fundus imaging
Glaucoma is one of the eye diseases stimulated by the fluid pressure that increases in the
eyes, damaging the optic nerves and causing partial or complete vision loss. As Glaucoma …
eyes, damaging the optic nerves and causing partial or complete vision loss. As Glaucoma …
GCNet: Grid-like context-aware network for RGB-thermal semantic segmentation
Semantic segmentation methods can achieve satisfactory performance under poor lighting
conditions by exploiting the complementary cues in RGB and thermal images. However …
conditions by exploiting the complementary cues in RGB and thermal images. However …
A knowledge-guided framework for fine-grained classification of liver lesions based on multi-phase CT images
Automatic and accurate differentiation of liver lesions from multi-phase computed
tomography imaging is critical for the early detection of liver cancer. Multi-phase data can …
tomography imaging is critical for the early detection of liver cancer. Multi-phase data can …
A Hunger Games Search algorithm with opposition-based learning for solving multimodal medical image registration
Multimodal medical image registration involves multiple pieces of medical equipment
collecting complementary information on the same content, resulting in information fusion …
collecting complementary information on the same content, resulting in information fusion …
Difference-deformable convolution with pseudo scale instance map for cell localization
Cell localization still faces two unresolved challenges: 1) the dramatic variations in cell
morphology, coupled with the heterogeneous intensity distribution of lightly stained cells; 2) …
morphology, coupled with the heterogeneous intensity distribution of lightly stained cells; 2) …
Multi-view coupled self-attention network for pulmonary nodules classification
Abstract Evaluation of the malignant degree of pulmonary nodules plays an important role in
early detecting lung cancer. Deep learning-based methods have obtained promising results …
early detecting lung cancer. Deep learning-based methods have obtained promising results …
Synergistic registration of CT-MRI brain images and retinal images: A novel approach leveraging reinforcement learning and modified artificial rabbit optimization
Medical image registration is a pivotal application within the field of medical imaging. It
entails the fusion of commonalities among data of disparate modalities into a unified …
entails the fusion of commonalities among data of disparate modalities into a unified …
A new multi-atlas based deep learning segmentation framework with differentiable atlas feature warping
Deep learning based multi-atlas segmentation (DL-MA) has achieved the state-of-the-art
performance in many medical image segmentation tasks, eg, brain parcellation. In DL-MA …
performance in many medical image segmentation tasks, eg, brain parcellation. In DL-MA …
Selfmix: a self-adaptive data augmentation method for lesion segmentation
Deep learning-based methods have obtained promising results in various organ
segmentation tasks, due to their effectiveness in learning feature representation. However …
segmentation tasks, due to their effectiveness in learning feature representation. However …
[HTML][HTML] Anatomically plausible segmentations: Explicitly preserving topology through prior deformations
Since the rise of deep learning, new medical segmentation methods have rapidly been
proposed with extremely promising results, often reporting marginal improvements on the …
proposed with extremely promising results, often reporting marginal improvements on the …