[HTML][HTML] Deep neural network pulmonary nodule segmentation methods for CT images: Literature review and experimental comparisons
L Zhi, W Jiang, S Zhang, T Zhou - Computers in Biology and Medicine, 2023 - Elsevier
Automatic and accurate segmentation of pulmonary nodules in CT images can help
physicians perform more accurate quantitative analysis, diagnose diseases, and improve …
physicians perform more accurate quantitative analysis, diagnose diseases, and improve …
Deep learning in breast cancer imaging: A decade of progress and future directions
Breast cancer has reached the highest incidence rate worldwide among all malignancies
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
Medical image segmentation review: The success of u-net
Automatic medical image segmentation is a crucial topic in the medical domain and
successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the …
successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the …
Cdnet: Centripetal direction network for nuclear instance segmentation
Nuclear instance segmentation is a challenging task due to a large number of touching and
overlapping nuclei in pathological images. Existing methods cannot effectively recognize the …
overlapping nuclei in pathological images. Existing methods cannot effectively recognize the …
Inter-and intra-uncertainty based feature aggregation model for semi-supervised histopathology image segmentation
Acquiring pixel-level annotations is often limited in applications such as histology studies
that require domain expertise. Various semi-supervised learning approaches have been …
that require domain expertise. Various semi-supervised learning approaches have been …
Semi-supervised histological image segmentation via hierarchical consistency enforcement
Acquiring pixel-level annotations for histological image segmentation is time-and labor-
consuming. Semi-supervised learning enables learning from the unlabeled and limited …
consuming. Semi-supervised learning enables learning from the unlabeled and limited …
DDU-Net: A dual dense U-structure network for medical image segmentation
Medical image segmentation is one of the important steps in medical image analysis and
has a wide range of applications and research values in medical research and practice …
has a wide range of applications and research values in medical research and practice …
SAC-Net: Learning with weak and noisy labels in histopathology image segmentation
Deep convolutional neural networks have been highly effective in segmentation tasks.
However, segmentation becomes more difficult when training images include many complex …
However, segmentation becomes more difficult when training images include many complex …
Bix-nas: Searching efficient bi-directional architecture for medical image segmentation
The recurrent mechanism has recently been introduced into U-Net in various medical image
segmentation tasks. Existing studies have focused on promoting network recursion via …
segmentation tasks. Existing studies have focused on promoting network recursion via …
MaxViT-UNet: Multi-axis attention for medical image segmentation
Since their emergence, Convolutional Neural Networks (CNNs) have made significant
strides in medical image analysis. However, the local nature of the convolution operator may …
strides in medical image analysis. However, the local nature of the convolution operator may …