Deep neural network models for computational histopathology: A survey
CL Srinidhi, O Ciga, AL Martel - Medical image analysis, 2021 - Elsevier
Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …
underlying mechanisms contributing to disease progression and patient survival outcomes …
Emerging role of deep learning‐based artificial intelligence in tumor pathology
The development of digital pathology and progression of state‐of‐the‐art algorithms for
computer vision have led to increasing interest in the use of artificial intelligence (AI) …
computer vision have led to increasing interest in the use of artificial intelligence (AI) …
Data-efficient and weakly supervised computational pathology on whole-slide images
Deep-learning methods for computational pathology require either manual annotation of
gigapixel whole-slide images (WSIs) or large datasets of WSIs with slide-level labels and …
gigapixel whole-slide images (WSIs) or large datasets of WSIs with slide-level labels and …
Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks
Traditional image-based survival prediction models rely on discriminative patch labeling
which make those methods not scalable to extend to large datasets. Recent studies have …
which make those methods not scalable to extend to large datasets. Recent studies have …
GasHis-Transformer: A multi-scale visual transformer approach for gastric histopathological image detection
In this paper, a multi-scale visual transformer model, referred as GasHis-Transformer, is
proposed for Gastric Histopathological Image Detection (GHID), which enables the …
proposed for Gastric Histopathological Image Detection (GHID), which enables the …
Deep learning technology for improving cancer care in society: New directions in cancer imaging driven by artificial intelligence
M Coccia - Technology in Society, 2020 - Elsevier
The goal of this study is to show emerging applications of deep learning technology in
cancer imaging. Deep learning technology is a family of computational methods that allow …
cancer imaging. Deep learning technology is a family of computational methods that allow …
A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches
With the development of Computer-aided Diagnosis (CAD) and image scanning techniques,
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …
[HTML][HTML] Artificial intelligence in gastroenterology: A state-of-the-art review
PT Kröner, MML Engels, BS Glicksberg… - World journal of …, 2021 - ncbi.nlm.nih.gov
The development of artificial intelligence (AI) has increased dramatically in the last 20 years,
with clinical applications progressively being explored for most of the medical specialties …
with clinical applications progressively being explored for most of the medical specialties …
Deep learning for computational cytology: A survey
Computational cytology is a critical, rapid-developing, yet challenging topic in medical
image computing concerned with analyzing digitized cytology images by computer-aided …
image computing concerned with analyzing digitized cytology images by computer-aided …
A multi-resolution model for histopathology image classification and localization with multiple instance learning
Large numbers of histopathological images have been digitized into high resolution whole
slide images, opening opportunities in developing computational image analysis tools to …
slide images, opening opportunities in developing computational image analysis tools to …