A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches
With the development of Computer-aided Diagnosis (CAD) and image scanning techniques,
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …
[HTML][HTML] Fine-tuned DenseNet-169 for breast cancer metastasis prediction using FastAI and 1-cycle policy
Lymph node metastasis in breast cancer may be accurately predicted using a DenseNet-
169 model. However, the current system for identifying metastases in a lymph node is …
169 model. However, the current system for identifying metastases in a lymph node is …
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
G Campanella, MG Hanna, L Geneslaw, A Miraflor… - Nature medicine, 2019 - nature.com
The development of decision support systems for pathology and their deployment in clinical
practice have been hindered by the need for large manually annotated datasets. To …
practice have been hindered by the need for large manually annotated datasets. To …
Machine learning methods for histopathological image analysis: A review
Histopathological images (HIs) are the gold standard for evaluating some types of tumors for
cancer diagnosis. The analysis of such images is time and resource-consuming and very …
cancer diagnosis. The analysis of such images is time and resource-consuming and very …
Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer
S Brockmoeller, A Echle, N Ghaffari Laleh… - The Journal of …, 2022 - Wiley Online Library
The spread of early‐stage (T1 and T2) adenocarcinomas to locoregional lymph nodes is a
key event in disease progression of colorectal cancer (CRC). The cellular mechanisms …
key event in disease progression of colorectal cancer (CRC). The cellular mechanisms …
Predicting breast cancer recurrence and metastasis risk by integrating color and texture features of histopathological images and machine learning technologies
X Liu, P Yuan, R Li, D Zhang, J An, J Ju, C Liu… - Computers in biology …, 2022 - Elsevier
Abstract About 30%–40% breast cancer patients suffer from recurrence and metastasis,
even after targeted therapy like trastuzumab. Since breast cancer recurrence and metastasis …
even after targeted therapy like trastuzumab. Since breast cancer recurrence and metastasis …
Multi-radial LBP features as a tool for rapid glomerular detection and assessment in whole slide histopathology images
We demonstrate a simple and effective automated method for the localization of glomeruli in
large (~ 1 gigapixel) histopathological whole-slide images (WSIs) of thin renal tissue …
large (~ 1 gigapixel) histopathological whole-slide images (WSIs) of thin renal tissue …
Cervical cancer metastasis and recurrence risk prediction based on deep convolutional neural network
Z Ye, Y Zhang, Y Liang, J Lang, X Zhang… - Current …, 2022 - ingentaconnect.com
Background: Evaluating the risk of metastasis and recurrence of a cervical cancer patient is
critical for appropriate adjuvant therapy. However, current risk assessment models usually …
critical for appropriate adjuvant therapy. However, current risk assessment models usually …
Prediction of early colorectal cancer metastasis by machine learning using digital slide images
M Takamatsu, N Yamamoto, H Kawachi… - Computer methods and …, 2019 - Elsevier
Background and objectives Prediction of lymph node metastasis (LNM) for early colorectal
cancer (CRC) is critical for determining treatment strategies after endoscopic resection …
cancer (CRC) is critical for determining treatment strategies after endoscopic resection …
[HTML][HTML] Blind color deconvolution, normalization, and classification of histological images using general super Gaussian priors and Bayesian inference
F Pérez-Bueno, M Vega, MA Sales… - Computer Methods and …, 2021 - Elsevier
Abstract Background and Objective: Color variations in digital histopathology severely
impact the performance of computer-aided diagnosis systems. They are due to differences in …
impact the performance of computer-aided diagnosis systems. They are due to differences in …