CODEX multiplexed tissue imaging with DNA-conjugated antibodies
Advances in multiplexed imaging technologies have drastically improved our ability to
characterize healthy and diseased tissues at the single-cell level. Co-detection by indexing …
characterize healthy and diseased tissues at the single-cell level. Co-detection by indexing …
[HTML][HTML] Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: An overview
J Gao, Q Jiang, B Zhou, D Chen - Mathematical Biosciences and …, 2019 - aimspress.com
Computer-aided detection or diagnosis (CAD) has been a promising area of research over
the last two decades. Medical image analysis aims to provide a more efficient diagnostic and …
the last two decades. Medical image analysis aims to provide a more efficient diagnostic and …
AI-based carcinoma detection and classification using histopathological images: A systematic review
Histopathological image analysis is the gold standard to diagnose cancer. Carcinoma is a
subtype of cancer that constitutes more than 80% of all cancer cases. Squamous cell …
subtype of cancer that constitutes more than 80% of all cancer cases. Squamous cell …
[HTML][HTML] Deep learning approach for accurate prostate cancer identification and stratification using combined immunostaining of cytokeratin, p63, and racemase
Abstract Background Prostate cancer (PCa) is the most frequently diagnosed cancer in men
worldwide, affecting around 1.4 million individuals. Current PCa diagnosis relies on …
worldwide, affecting around 1.4 million individuals. Current PCa diagnosis relies on …
Radiomics-based machine learning models for predicting P504s/P63 Immunohistochemical Expression: a noninvasive diagnostic tool for prostate cancer
YF Liu, X Shu, XF Qiao, GY Ai, L Liu, J Liao… - Frontiers in …, 2022 - frontiersin.org
Objective To develop and validate a noninvasive radiomic-based machine learning (ML)
model to identify P504s/P63 status and further achieve the diagnosis of prostate cancer …
model to identify P504s/P63 status and further achieve the diagnosis of prostate cancer …
Artificial intelligence–based algorithms for the diagnosis of prostate cancer: A systematic review
S Marletta, A Eccher, FM Martelli… - American Journal of …, 2024 - academic.oup.com
Objectives The high incidence of prostate cancer causes prostatic samples to significantly
affect pathology laboratories workflow and turnaround times (TATs). Whole-slide imaging …
affect pathology laboratories workflow and turnaround times (TATs). Whole-slide imaging …
[HTML][HTML] Automatic detection of prostate cancer grades and chronic prostatitis in biparametric MRI
Abstract Background and Objective: With emerging evidence to improve prostate cancer
(PCa) screening, multiparametric magnetic prostate imaging is becoming an essential …
(PCa) screening, multiparametric magnetic prostate imaging is becoming an essential …
System for quantitative evaluation of DAB&H-stained breast cancer biopsy digital images (CHISEL)
This study presents CHISEL (Computer-assisted Histopathological Image Segmentation and
EvaLuation), an end-to-end system capable of quantitative evaluation of benign and …
EvaLuation), an end-to-end system capable of quantitative evaluation of benign and …
Evaluation of elite Oryza sativa L. germplasm and development of an effective hematoxylin stain-based screen for NaCl-tolerant rice at the Germination stage
N Kruthika, JM Narayanan - Genetic Resources and Crop Evolution, 2024 - Springer
One important feature distinguishing plants from other multicellular organisms is that plants
are sessile and must endure diverse environmental challenges. Among the abiotic stressors …
are sessile and must endure diverse environmental challenges. Among the abiotic stressors …
Cell Nucleus-Graph Convolutional Network Evaluation of Immunohistochemistry Images of Colorectal Cancer
Q Huang, Z Mo, W Ge, D Hou, G Zhang - Authorea Preprints, 2023 - techrxiv.org
This study proposed a method for interpreting immunohistochemistry (IHC) images based on
a graph convolutional network (GCN). Self-supervised transfer learning was employed to …
a graph convolutional network (GCN). Self-supervised transfer learning was employed to …