Classification and grade prediction of kidney cancer histological images using deep learning
Abstract Renal Cell Carcinoma (RCC) is the most common malignant tumor (85%) of kidney
cancer and has a complex histological pattern and nuclear structure. The manual diagnosis …
cancer and has a complex histological pattern and nuclear structure. The manual diagnosis …
A Survey on Cell Nuclei Instance Segmentation and Classification: Leveraging Context and Attention
Manually annotating nuclei from the gigapixel Hematoxylin and Eosin (H&E)-stained Whole
Slide Images (WSIs) is a laborious and costly task, meaning automated algorithms for cell …
Slide Images (WSIs) is a laborious and costly task, meaning automated algorithms for cell …
DaCSeg: Divide and conquer for accurate overlapping chromosome instance segmentation in metaphase cell images
X Fan, H Liu, H Zheng, J Zhai, L Zhang - Biomedical Signal Processing and …, 2024 - Elsevier
Chromosome segmentation is essential for karyotyping analysis, which provides a reliable
basis for subsequent disease diagnosis. Recent deep segmentation techniques can …
basis for subsequent disease diagnosis. Recent deep segmentation techniques can …
From Pixels to Prognosis: A Survey on AI-Driven Cancer Patient Survival Prediction Using Digital Histology Images
Survival analysis is an integral part of medical statistics that is extensively utilized to
establish prognostic indices for mortality or disease recurrence, assess treatment efficacy …
establish prognostic indices for mortality or disease recurrence, assess treatment efficacy …
Combining Datasets with Different Label Sets for Improved Nucleus Segmentation and Classification
A Parulekar, U Kanwat, RK Gupta, M Chippa… - arXiv preprint arXiv …, 2023 - arxiv.org
Segmentation and classification of cell nuclei in histopathology images using deep neural
networks (DNNs) can save pathologists' time for diagnosing various diseases, including …
networks (DNNs) can save pathologists' time for diagnosing various diseases, including …
[HTML][HTML] Deep network-based method and software for small sample biomedical image generation and classification
Context. The authors of the article investigated the problem of generating and classifying
breast cancer histological images. The widespread incidence of breast cancer explains the …
breast cancer histological images. The widespread incidence of breast cancer explains the …
An Attention-Driven Hybrid Network for Survival Analysis of Tumorigenesis Patients Using Whole Slide Images
A Parvaiz, MM Fraz - Asian Conference on Intelligent Information and …, 2024 - Springer
Survival analysis of cancer patients using whole slide images (WSIs) is crucial in the field of
medical statistics, as it helps identify key prognostic factors related to mortality and disease …
medical statistics, as it helps identify key prognostic factors related to mortality and disease …
NuRISC: Nuclei Radial Instance Segmentation and Classification Check for updates
Accurate segmentation and classification of nuclei instances is one of the most challenging
tasks due to wide occurrence of overlapping, cluttered nuclei having blurred boundaries …
tasks due to wide occurrence of overlapping, cluttered nuclei having blurred boundaries …
NuRISC: Nuclei Radial Instance Segmentation and Classification
Accurate segmentation and classification of nuclei instances is one of the most challenging
tasks due to wide occurrence of overlapping, cluttered nuclei having blurred boundaries …
tasks due to wide occurrence of overlapping, cluttered nuclei having blurred boundaries …
Systematic PD-L1 Slide Analysis Based on Multi-Objective Learning.
C Zhao, GUO Danqi, W Qian, S Yiting… - Journal of Donghua …, 2024 - search.ebscohost.com
In treatment of cancers, especially non-small-cell lung cancers such as lung squamous cell
carcinoma (LUSC), tumor proportion score (TPS) of a programmed death-ligand 1 (PD-L1) …
carcinoma (LUSC), tumor proportion score (TPS) of a programmed death-ligand 1 (PD-L1) …