Deep contrastive learning based tissue clustering for annotation-free histopathology image analysis
Background: Deep convolutional neural networks (CNNs) have yielded promising results in
automatic whole slide images (WSIs) processing for digital pathology in recent years …
automatic whole slide images (WSIs) processing for digital pathology in recent years …
Pcgan: A noise robust conditional generative adversarial network for one shot learning
Traffic sign classification plays a vital role in autonomous vehicles for its powerful capability
in information representation. However, the low-quality data of traffic signs captured by in …
in information representation. However, the low-quality data of traffic signs captured by in …
PathNarratives: Data annotation for pathological human-AI collaborative diagnosis
Pathology is the gold standard of clinical diagnosis. Artificial intelligence (AI) in pathology
becomes a new trend, but it is still not widely used due to the lack of necessary explanations …
becomes a new trend, but it is still not widely used due to the lack of necessary explanations …
Deep learning for liver cancer histopathology image analysis: A comprehensive survey
Liver cancer is the predominant cause of cancer-related fatalities globally, wherein
Hepatocellular Carcinoma (HCC) and Intrahepatic Cholangiocarcinoma (ICC) emerge as …
Hepatocellular Carcinoma (HCC) and Intrahepatic Cholangiocarcinoma (ICC) emerge as …
Maximum mean discrepancy kernels for predictive and prognostic modeling of whole slide images
How similar are two images? In computational pathology, where Whole Slide Images (WSIs)
of digitally scanned tissue samples from patients can be multi-gigapixels in size …
of digitally scanned tissue samples from patients can be multi-gigapixels in size …
Multi-scale spatial consistency for deep semi-supervised skin lesion segmentation
This paper introduces a novel semi-supervised framework, the Multiscale Spatial
Consistency Network (MSCNet), for robust semi-supervised skin lesion segmentation …
Consistency Network (MSCNet), for robust semi-supervised skin lesion segmentation …
PS-Net: human perception-guided segmentation network for EM cell membrane
Motivation Cell membrane segmentation in electron microscopy (EM) images is a crucial
step in EM image processing. However, while popular approaches have achieved …
step in EM image processing. However, while popular approaches have achieved …
A novel automatic annotation method for whole slide pathological images combined clustering and edge detection technique
W Ding, W Liao, X Zhu, H Zhu - IET Image Processing, 2024 - Wiley Online Library
Pixel‐level labeling of regions of interest in an image is a key step in building a labeled
training dataset for supervised deep learning networks of images. However, traditional …
training dataset for supervised deep learning networks of images. However, traditional …
A Virtual Staining Method for Immunohistochemical Images of Breast Cancer
Breast cancer is a common malignant cancer. Detection of human epidermal growth factor
receptor 2 (HER2) status based on immunohistochemistry (IHC) is an effective method for …
receptor 2 (HER2) status based on immunohistochemistry (IHC) is an effective method for …
Towards better dermoscopic image feature representation learning for melanoma classification
Deep learning-based melanoma classification with dermoscopic images has recently shown
great potential in automatic early-stage melanoma diagnosis. However, limited by the …
great potential in automatic early-stage melanoma diagnosis. However, limited by the …