A comprehensive review of deep learning in colon cancer
Deep learning has emerged as a leading machine learning tool in object detection and has
attracted attention with its achievements in progressing medical image analysis …
attracted attention with its achievements in progressing medical image analysis …
TSHVNet: simultaneous nuclear instance segmentation and classification in histopathological images based on multiattention mechanisms
Y Chen, Y Jia, X Zhang, J Bai, X Li… - BioMed Research …, 2022 - Wiley Online Library
Accurate nuclear instance segmentation and classification in histopathologic images are the
foundation of cancer diagnosis and prognosis. Several challenges are restricting the …
foundation of cancer diagnosis and prognosis. Several challenges are restricting the …
[HTML][HTML] Nextflow pipeline for Visium and H&E data from patient-derived xenograft samples
We designed a Nextflow DSL2-based pipeline, Spatial Transcriptomics Quantification (STQ),
for simultaneous processing of 10x Genomics Visium spatial transcriptomics data and a …
for simultaneous processing of 10x Genomics Visium spatial transcriptomics data and a …
HAPPY: A deep learning pipeline for mapping cell-to-tissue graphs across placenta histology whole slide images
C Vanea, J Džigurski, V Rukins, O Dodi, S Siigur… - bioRxiv, 2022 - biorxiv.org
Accurate placenta pathology assessment is essential for managing maternal and newborn
health, but the placenta's heterogeneity and temporal variability pose challenges for …
health, but the placenta's heterogeneity and temporal variability pose challenges for …
Active Learning Enabled Low-cost Cell Image Segmentation Using Bounding Box Annotation
Y Zhu, Q Yang, L Xu - arXiv preprint arXiv:2405.01701, 2024 - arxiv.org
Cell image segmentation is usually implemented using fully supervised deep learning
methods, which heavily rely on extensive annotated training data. Yet, due to the complexity …
methods, which heavily rely on extensive annotated training data. Yet, due to the complexity …
[HTML][HTML] Svetlana a supervised segmentation classifier for Napari
Abstract We present Svetlana (SuperVised sEgmenTation cLAssifier for NapAri), an open-
source Napari plugin dedicated to the manual or automatic classification of segmentation …
source Napari plugin dedicated to the manual or automatic classification of segmentation …
Revolutionizing Cancer Diagnosis Through Hybrid Self-supervised Deep Learning: EfficientNet with Denoising Autoencoder for Semantic Segmentation of …
Abstract Machine Learning technologies are being developed day after day, especially in
the medical field. New approaches, algorithms and architectures are implemented to …
the medical field. New approaches, algorithms and architectures are implemented to …
Efficient Semantic Segmentation of Nuclei in Histopathology Images Using Segformer
Segmentation of nuclei in histopathology images with high accuracy is crucial for the
diagnosis and prognosis of cancer and other diseases. Using Artificial Intelligence (AI) in the …
diagnosis and prognosis of cancer and other diseases. Using Artificial Intelligence (AI) in the …
Deceptive learning in histopathology
S Shahamatdar, D Saeed‐Vafa, D Linsley… - …, 2024 - Wiley Online Library
Aims Deep learning holds immense potential for histopathology, automating tasks that are
simple for expert pathologists and revealing novel biology for tasks that were previously …
simple for expert pathologists and revealing novel biology for tasks that were previously …
Active Learning Enabled Low-Cost Cell Image Segmentation Using Bounding Box Annotation
Q Yang, L Xu - papers.ssrn.com
Cell image segmentation is usually implemented using fully supervised deep learning
methods, which heavily rely on extensive annotated training data. Yet, due to the complexity …
methods, which heavily rely on extensive annotated training data. Yet, due to the complexity …