Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review
JP Redlich, F Feuerhake, J Weis, NS Schaadt… - npj Imaging, 2024 - nature.com
In recent years, the diagnosis of gliomas has become increasingly complex. Analysis of
glioma histopathology images using artificial intelligence (AI) offers new opportunities to …
glioma histopathology images using artificial intelligence (AI) offers new opportunities to …
Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images
Current diagnosis of glioma types requires combining both histological features and
molecular characteristics, which is an expensive and time-consuming procedure …
molecular characteristics, which is an expensive and time-consuming procedure …
Vision transformer based classification of gliomas from histopathological images
E Goceri - Expert Systems with Applications, 2024 - Elsevier
Early and accurate detection and classification of glioma types is of paramount importance
in determining treatment planning and increasing the survival rate of patients. At present …
in determining treatment planning and increasing the survival rate of patients. At present …
Towards machine learning-based quantitative hyperspectral image guidance for brain tumor resection
Background Complete resection of malignant gliomas is hampered by the difficulty in
distinguishing tumor cells at the infiltration zone. Fluorescence guidance with 5-ALA assists …
distinguishing tumor cells at the infiltration zone. Fluorescence guidance with 5-ALA assists …
Artificial intelligence in histopathological image analysis of central nervous system tumours: A systematic review
MP Jensen, Z Qiang, DZ Khan… - Neuropathology and …, 2024 - Wiley Online Library
The convergence of digital pathology and artificial intelligence could assist histopathology
image analysis by providing tools for rapid, automated morphological analysis. This …
image analysis by providing tools for rapid, automated morphological analysis. This …
Domain-specific pre-training improves confidence in whole slide image classification
Whole Slide Images (WSIs) or histopathology images are used in digital pathology. WSIs
pose great challenges to deep learning models for clinical diagnosis, owing to their size and …
pose great challenges to deep learning models for clinical diagnosis, owing to their size and …
Tpmil: Trainable prototype enhanced multiple instance learning for whole slide image classification
Digital pathology based on whole slide images (WSIs) plays a key role in cancer diagnosis
and clinical practice. Due to the high resolution of the WSI and the unavailability of patch …
and clinical practice. Due to the high resolution of the WSI and the unavailability of patch …
Attention-based deep learning approaches in brain tumor image analysis: A mini review
Introduction: Accurate diagnosis is crucial for brain tumors, given their low survival rates and
high treatment costs. However, traditional methods relying on manual interpretation of …
high treatment costs. However, traditional methods relying on manual interpretation of …
Practical Application of Deep Learning in Diagnostic Neuropathology—Reimagining a Histological Asset in the Era of Precision Medicine
Simple Summary Technological and scientific innovations, from genetic sequencing to
digital pathology slide scanners, have drastically altered the field of neuropathology. The …
digital pathology slide scanners, have drastically altered the field of neuropathology. The …
[HTML][HTML] Deep Residual Learning-Based Classification with Identification of Incorrect Predictions and Quantification of Cellularity and Nuclear Morphological Features …
Simple Summary This study presented an artificial intelligence-based classification
emphasizing error identification and quantification of cellularity and nuclear morphological …
emphasizing error identification and quantification of cellularity and nuclear morphological …