Histopathological Image Classification
21 papers with code • 0 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Histopathological Image Classification
Chaoyang
DiagSet
BCNB
Latest papers with no code
Supervised Contrastive Vision Transformer for Breast Histopathological Image Classification
We present a novel approach, Supervised Contrastive Vision Transformer (SupCon-ViT), for improving the classification of invasive ductal carcinoma in terms of accuracy and generalization by leveraging the inherent strengths and advantages of both transfer learning, i. e., pre-trained vision transformer, and supervised contrastive learning.
Focused Active Learning for Histopathological Image Classification
The lack of precise uncertainty estimations leads to the acquisition of images with a low informative value.
A Novel Approach to Breast Cancer Histopathological Image Classification Using Cross-Colour Space Feature Fusion and Quantum-Classical Stack Ensemble Method
Breast cancer classification stands as a pivotal pillar in ensuring timely diagnosis and effective treatment.
Histopathological Image Classification and Vulnerability Analysis using Federated Learning
Although FL can protect user privacy for healthcare diagnostics, it is also vulnerable to data poisoning, which must be addressed.
Asymmetric Co-Training with Explainable Cell Graph Ensembling for Histopathological Image Classification
To make full use of pixel-level and cell-level features dynamically, we propose an asymmetric co-training framework combining a deep graph convolutional network and a convolutional neural network for multi-class histopathological image classification.
Breast Cancer Detection and Diagnosis: A comparative study of state-of-the-arts deep learning architectures
By improving the workflow of pathologists, these AI models have the potential to enhance the detection and diagnosis of breast cancer.
IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach
In addition, we conducted an ablation experiment and an interchangeability experiment to verify the ability and interchangeability of the three channels.
Application of Graph Based Features in Computer Aided Diagnosis for Histopathological Image Classification of Gastric Cancer
The gold standard for gastric cancer detection is gastric histopathological image analysis, but there are certain drawbacks in the existing histopathological detection and diagnosis.
Self-distillation Augmented Masked Autoencoders for Histopathological Image Classification
In this paper, we introduce MAE and verify the effect of visible patches for histopathological image understanding.
GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection
In this paper, a multi-scale visual transformer model, referred as GasHis-Transformer, is proposed for Gastric Histopathological Image Detection (GHID), which enables the automatic global detection of gastric cancer images.
