Deep learning for whole slide image analysis: an overview
The widespread adoption of whole slide imaging has increased the demand for effective
and efficient gigapixel image analysis. Deep learning is at the forefront of computer vision …
and efficient gigapixel image analysis. Deep learning is at the forefront of computer vision …
Deep learning with multimodal representation for pancancer prognosis prediction
A Cheerla, O Gevaert - Bioinformatics, 2019 - academic.oup.com
Motivation Estimating the future course of patients with cancer lesions is invaluable to
physicians; however, current clinical methods fail to effectively use the vast amount of …
physicians; however, current clinical methods fail to effectively use the vast amount of …
医学图像处理中的注意力机制研究综述.
陈朝一， 许波， 吴英， 吴凯文 - Journal of Computer …, 2022 - search.ebscohost.com
注意力机制通过对深度学习模型判断的可视化, 有望成为将深度学习应用于临床实践的安全支撑
. 通过结合注意力机制, 不仅可以验证深度学习模型的判断依据, 而且可以让深度学习模型更多地 …
. 通过结合注意力机制, 不仅可以验证深度学习模型的判断依据, 而且可以让深度学习模型更多地 …
Predicting prognosis and IDH mutation status for patients with lower-grade gliomas using whole slide images
We developed end-to-end deep learning models using whole slide images of adults
diagnosed with diffusely infiltrating, World Health Organization (WHO) grade 2 gliomas to …
diagnosed with diffusely infiltrating, World Health Organization (WHO) grade 2 gliomas to …
[HTML][HTML] Deep learning enables the automation of grading histological tissue engineered cartilage images for quality control standardization
L Power, L Acevedo, R Yamashita, D Rubin… - Osteoarthritis and …, 2021 - Elsevier
Objective To automate the grading of histological images of engineered cartilage tissues
using deep learning. Methods Cartilaginous tissues were engineered from various cell …
using deep learning. Methods Cartilaginous tissues were engineered from various cell …
Multi-scale feature fusion for prediction of IDH1 mutations in glioma histopathological images
X Liu, W Hu, S Diao, DE Abera, D Racoceanu… - Computer Methods and …, 2024 - Elsevier
Background and objective Mutations in isocitrate dehydrogenase 1 (IDH1) play a crucial role
in the prognosis, diagnosis, and treatment of gliomas. However, current methods for …
in the prognosis, diagnosis, and treatment of gliomas. However, current methods for …
Biomarker-based classification and localization of renal lesions using learned representations of histology—a machine learning approach to histopathology
CAC Freyre, S Spiegel, C Gubser Keller… - Toxicologic …, 2021 - journals.sagepub.com
Several deep learning approaches have been proposed to address the challenges in
computational pathology by learning structural details in an unbiased way. Transfer learning …
computational pathology by learning structural details in an unbiased way. Transfer learning …
Review of reinforcement learning applications in segmentation, chemotherapy, and radiotherapy of cancer
R Khajuria, A Sarwar - Micron, 2023 - Elsevier
Owing to early diagnosis and treatment of cancer as a prerequisite in recent times, the role
of machine learning has been increased substantially. The mathematically powerful and …
of machine learning has been increased substantially. The mathematically powerful and …
Pyramidal position attention model for histopathological image segmentation
The level of performance achieved in the classification of histopathological images has not
yet been reached in the segmentation area. This is because the global context information …
yet been reached in the segmentation area. This is because the global context information …
CroMAM: A Cross-Magnification Attention Feature Fusion Model for Predicting Genetic Status and Survival of Gliomas using Histological Images
Predicting the gene mutation status in whole slide images (WSI) is crucial for the clinical
treatment, cancer management, and research of gliomas. With advancements in CNN and …
treatment, cancer management, and research of gliomas. With advancements in CNN and …