Advantages of transformer and its application for medical image segmentation: a survey
Purpose Convolution operator-based neural networks have shown great success in medical
image segmentation over the past decade. The U-shaped network with a codec structure is …
image segmentation over the past decade. The U-shaped network with a codec structure is …
Vision transformer based classification of gliomas from histopathological images
E Goceri - Expert Systems with Applications, 2024 - Elsevier
Early and accurate detection and classification of glioma types is of paramount importance
in determining treatment planning and increasing the survival rate of patients. At present …
in determining treatment planning and increasing the survival rate of patients. At present …
Vision transformer promotes cancer diagnosis: A comprehensive review
Background The approaches based on vision transformers (ViTs) are advancing the field of
medical artificial intelligence (AI) and cancer diagnosis. Recently, many researchers have …
medical artificial intelligence (AI) and cancer diagnosis. Recently, many researchers have …
Equipping computational pathology systems with artifact processing pipelines: a showcase for computation and performance trade-offs
Background Histopathology is a gold standard for cancer diagnosis. It involves extracting
tissue specimens from suspicious areas to prepare a glass slide for a microscopic …
tissue specimens from suspicious areas to prepare a glass slide for a microscopic …
Ensemble transformer-based multiple instance learning to predict pathological subtypes and tumor mutational burden from histopathological whole slide images of …
In endometrial cancer (EC) and colorectal cancer (CRC), in addition to microsatellite
instability, tumor mutational burden (TMB) has gradually gained attention as a genomic …
instability, tumor mutational burden (TMB) has gradually gained attention as a genomic …
CViTS-Net: A CNN-ViT Network with Skip Connections for Histopathology Image Classification
A Kanadath, JAA Jothi, S Urolagin - IEEE Access, 2024 - ieeexplore.ieee.org
Histopathological image classification stands as a cornerstone in the pathological diagnosis
workflow, yet it remains challenging due to the inherent complexity of histopathological …
workflow, yet it remains challenging due to the inherent complexity of histopathological …
A lightweight spatially-aware classification model for breast cancer pathology images
L Jiang, C Zhang, H Zhang, H Cao - Biocybernetics and Biomedical …, 2024 - Elsevier
Breast cancer is a prevalent malignant tumour with high global incidence. Its diagnosis
relies primarily on the analysis of pathological breast images. Owing to the complex …
relies primarily on the analysis of pathological breast images. Owing to the complex …
IHCSurv: Effective Immunohistochemistry Priors for Cancer Survival Analysis in Gigapixel Multi-stain Whole Slide Images
Recent cancer survival prediction approaches have made great strides in analyzing H&E-
stained gigapixel whole-slide images. However, methods targeting the …
stained gigapixel whole-slide images. However, methods targeting the …
Impact of imperfect annotations on CNN training and performance for instance segmentation and classification in digital pathology
LG Jiménez, C Decaestecker - Computers in Biology and Medicine, 2024 - Elsevier
Segmentation and classification of large numbers of instances, such as cell nuclei, are
crucial tasks in digital pathology for accurate diagnosis. However, the availability of high …
crucial tasks in digital pathology for accurate diagnosis. However, the availability of high …
Spatially-constrained and-unconstrained bi-graph interaction network for multi-organ pathology image classification
In computational pathology, graphs have shown to be promising for pathology image
analysis. There exist various graph structures that can discover differing features of …
analysis. There exist various graph structures that can discover differing features of …