A Web/Cloud based Digital Pathology Platform Framework for AI Development and Deployment
Akkus, Z.
Show abstract
Digitization of glass slides has brought several opportunities with it for computational pathology and artificial intelligence (AI). The application of AI in digital pathology slides shows potential for QA/QC, triaging cases, and assisting pathologists in clinical decision making. We present an extensible and modular web/cloud based digital pathology framework for AI development and deployment. The proposed platform supports collaborative multi-user and multi-device annotation, remote slide access, and remote telepathology or teleconsultation tasks.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Using an Anomaly Detection Approach for the Segmentation of Colorectal Cancer Tumors in Whole Slide Images 95%
- Development of an Interactive Web Dashboard to Facilitate the Reexamination of Pathology Reports for Instances of Underbilling of CPT Codes 95%
- Independent assessment of a deep learning system for lymph node metastasis detection on the Augmented Reality Microscope 95%
Similar papers in this journal
- Weakly supervised learning for multi-organ adenocarcinoma classification in whole slide images 94%
- Navigated ultrasound bronchoscopy with integrated positron emission tomography - A human feasibility study 94%
- ai-corona : Radiologist-Assistant Deep Learning Framework for COVID-19 Diagnosis in Chest CT Scans 94%
Similar papers in this journal
- On evaluation metrics for medical applications of artificial intelligence 94%
- Toward Understanding COVID-19 Pneumonia: A Deep-learning-based Approach for Severity Analysis and Monitoring the Disease 94%
- Segmentation of Pancreatic Ductal Adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and Texture Descriptors 93%
Similar papers in this journal
- Demarcation line determination for diagnosis of gastric cancer disease range using unsupervised machine learning in magnifying narrow-band imaging 93%
- Auto-detection of motion artifacts on CT pulmonary angiograms with a physician-trained AI algorithm 93%
- Detection, Isolation and Quantification of Myocardial Infarct with Four Different Histological Staining Techniques 92%
Similar papers in this journal
- The potential for digital patient symptom recording through symptom assessment applications to optimize patient flow and reduce waiting times in Urgent Care Centers: a simulation study 89%
- Multimodal Pain Recognition in Postoperative Patients: A Machine Learning Approach 89%
- A Web-based, Mobile Responsive Application to Screen Healthcare Workers for COVID Symptoms: Descriptive Study 88%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.