Advantages of transformer and its application for medical image segmentation: a survey

Q Pu, Z Xi, S Yin, Z Zhao, L Zhao - BioMedical Engineering OnLine, 2024 - Springer
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 …

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 …

Vision transformer promotes cancer diagnosis: A comprehensive review

X Jiang, S Wang, Y Zhang - Expert Systems with Applications, 2024 - Elsevier
Background The approaches based on vision transformers (ViTs) are advancing the field of
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

N Kanwal, F Khoraminia, U Kiraz… - BMC Medical Informatics …, 2024 - Springer
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 …

Ensemble transformer-based multiple instance learning to predict pathological subtypes and tumor mutational burden from histopathological whole slide images of …

CW Wang, TC Liu, PJ Lai, H Muzakky, YC Wang… - Medical Image …, 2025 - Elsevier
In endometrial cancer (EC) and colorectal cancer (CRC), in addition to microsatellite
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 …

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 …

IHCSurv: Effective Immunohistochemistry Priors for Cancer Survival Analysis in Gigapixel Multi-stain Whole Slide Images

Y Zhang, H Chao, Z Qiu, W Liu, Y Shen… - … Conference on Medical …, 2024 - Springer
Recent cancer survival prediction approaches have made great strides in analyzing H&E-
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 …

Spatially-constrained and-unconstrained bi-graph interaction network for multi-organ pathology image classification

DC Bui, B Song, K Kim, JT Kwak - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
In computational pathology, graphs have shown to be promising for pathology image
analysis. There exist various graph structures that can discover differing features of …