Artificial intelligence to predict lymph node metastasis at CT in pancreatic ductal adenocarcinoma
Y Bian, Z Zheng, X Fang, H Jiang, M Zhu, J Yu, H Zhao… - Radiology, 2023 - pubs.rsna.org
Background Although deep learning has brought revolutionary changes in health care,
reliance on manually selected cross-sectional images and segmentation remain …
reliance on manually selected cross-sectional images and segmentation remain …
Multiplexed immunohistochemistry and digital pathology as the foundation for next-generation pathology in melanoma: methodological comparison and future clinical …
Y Van Herck, A Antoranz, MD Andhari, G Milli… - Frontiers in …, 2021 - frontiersin.org
The state-of-the-art for melanoma treatment has recently witnessed an enormous revolution,
evolving from a chemotherapeutic,“one-drug-for-all” approach, to a tailored molecular-and …
evolving from a chemotherapeutic,“one-drug-for-all” approach, to a tailored molecular-and …
Image analysis of cutaneous melanoma histology: a systematic review and meta-analysis
The current subjective histopathological assessment of cutaneous melanoma is challenging.
The application of image analysis algorithms to histological images may facilitate …
The application of image analysis algorithms to histological images may facilitate …
Deep reinforcement learning for weakly-supervised lymph node segmentation in CT images
Accurate and automated lymph node segmentation is pivotal for quantitatively accessing
disease progression and potential therapeutics. The complex variation of lymph node …
disease progression and potential therapeutics. The complex variation of lymph node …
An unsupervised method for histological image segmentation based on tissue cluster level graph cut
While deep learning models have demonstrated outstanding performance in medical image
segmentation tasks, histological annotations for training deep learning models are usually …
segmentation tasks, histological annotations for training deep learning models are usually …
A Review of Recent Advances in Computer-Aided Detection Methods Using Hyperspectral Imaging Engineering to Detect Skin Cancer
HY Huang, YP Hsiao, R Karmakar, A Mukundan… - Cancers, 2023 - mdpi.com
Simple Summary Recent advancements in this field have shown continuous improvement,
with CAD algorithms enhancing diagnosis accuracy. The study systematically analyzes …
with CAD algorithms enhancing diagnosis accuracy. The study systematically analyzes …
Detection of malignant melanoma in H&E-stained images using deep learning techniques
Histopathological images are widely used to diagnose diseases including skin cancer. As
digital histopathological images are typically of very large size, in the order of several billion …
digital histopathological images are typically of very large size, in the order of several billion …
Segmenting skin biopsy images with coarse and sparse annotations using U-Net
The number of melanoma diagnoses has increased dramatically over the past three
decades, outpacing almost all other cancers. Nearly 1 in 4 skin biopsies is of melanocytic …
decades, outpacing almost all other cancers. Nearly 1 in 4 skin biopsies is of melanocytic …
Detection of stages of melanoma using deep learning
Human Skin is the most utilized and largest organs next to blood acts as an outer protective
covering of the entire body to protect the underlying internal organs from harmful UV rays …
covering of the entire body to protect the underlying internal organs from harmful UV rays …
Deep learning-based histopathological image analysis for automated detection and staging of melanoma
With recent advances in digital microscopy, quantitative analysis of pathology images has
become important in disease diagnosis as well as in understanding the underlying reasons …
become important in disease diagnosis as well as in understanding the underlying reasons …