A survey of the vision transformers and their CNN-transformer based variants
Vision transformers have become popular as a possible substitute to convolutional neural
networks (CNNs) for a variety of computer vision applications. These transformers, with their …
networks (CNNs) for a variety of computer vision applications. These transformers, with their …
A survey on cell nuclei instance segmentation and classification: Leveraging context and attention
Nuclear-derived morphological features and biomarkers provide relevant insights regarding
the tumour microenvironment, while also allowing diagnosis and prognosis in specific …
the tumour microenvironment, while also allowing diagnosis and prognosis in specific …
Fastsam3d: An efficient segment anything model for 3d volumetric medical images
Segment anything models (SAMs) are gaining attention for their zero-shot generalization
capability in segmenting objects of unseen classes and in unseen domains when properly …
capability in segmenting objects of unseen classes and in unseen domains when properly …
Transnuseg: A lightweight multi-task transformer for nuclei segmentation
Nuclei appear small in size, yet, in real clinical practice, the global spatial information and
correlation of the color or brightness contrast between nuclei and background, have been …
correlation of the color or brightness contrast between nuclei and background, have been …
[HTML][HTML] Moe-nuseg: Enhancing nuclei segmentation in histology images with a two-stage mixture of experts network
Accurate nuclei segmentation is essential for extracting quantitative information from
histology images to support disease diagnosis and treatment decisions. However, precise …
histology images to support disease diagnosis and treatment decisions. However, precise …
[HTML][HTML] CompSegNet: An enhanced U-shaped architecture for nuclei segmentation in H&E histopathology images
In histopathology, nuclei within images hold vital diagnostic information. Automated
segmentation of nuclei can alleviate pathologists' workload and enhance diagnostic …
segmentation of nuclei can alleviate pathologists' workload and enhance diagnostic …
A 2.5 D multi-path fusion network framework with focusing on z-axis 3D joint for medical image segmentation
F Xia, Y Peng, J Wang, X Chen - Biomedical Signal Processing and Control, 2024 - Elsevier
Accurate segmentation of organs and tumors from medical images is to diagnose and treat
diseases more accurately. Many organs, such as the representative pancreas and spleen …
diseases more accurately. Many organs, such as the representative pancreas and spleen …
CB-HVTNet: A channel-boosted hybrid vision transformer network for lymphocyte assessment in histopathological images
Transformers, due to their ability to learn long range dependencies, have overcome the
shortcomings of convolutional neural networks (CNNs) for global perspective learning …
shortcomings of convolutional neural networks (CNNs) for global perspective learning …
Learnable color space conversion and fusion for stain normalization in pathology images
Variations in hue and contrast are common in H&E-stained pathology images due to
differences in slide preparation across various institutions. Such stain variations, while not …
differences in slide preparation across various institutions. Such stain variations, while not …
CNSeg: a dataset for cervical nuclear segmentation
J Zhao, Y He, SH Zhou, J Qin, Y Xie - Computer Methods and Programs in …, 2023 - Elsevier
Background and objective Nuclear segmentation in cervical cell images is a crucial
technique for automatic cytopathology diagnosis. Experimental evaluation of nuclear …
technique for automatic cytopathology diagnosis. Experimental evaluation of nuclear …