13 citations · 17 across the 6 of their papers we have counts for
6 papers
HistoKT: Cross Knowledge Transfer in Computational Pathology
Ryan Zhang, Jiadai Zhu, Stephen Yang +7
The lack of well-annotated datasets in computational pathology (CPath) obstructs the application of deep learning techniques for classifying medical images. %Since pathologist time…
Probeable DARTS with Application to Computational Pathology
Sheyang Tang, Mahdi S. Hosseini, Lina Chen +5
AI technology has made remarkable achievements in computational pathology (CPath), especially with the help of deep neural networks. However, the network performance is highly rela…
Fine-Tuning and Training of DenseNet for Histopathology Image Representation Using TCGA Diagnostic Slides
Abtin Riasatian, Morteza Babaie, Danial Maleki +19
Feature vectors provided by pre-trained deep artificial neural networks have become a dominant source for image representation in recent literature. Their contribution to the perfo…
Recognizing Magnification Levels in Microscopic Snapshots
Manit Zaveri, Shivam Kalra, Morteza Babaie +4
Recent advances in digital imaging has transformed computer vision and machine learning to new tools for analyzing pathology images. This trend could automate some of the tasks in…
Pan-Cancer Diagnostic Consensus Through Searching Archival Histopathology Images Using Artificial Intelligence
Shivam Kalra, H. R. Tizhoosh, Sultaan Shah +8
The emergence of digital pathology has opened new horizons for histopathology and cytology. Artificial-intelligence algorithms are able to operate on digitized slides to assist pat…
Focus Quality Assessment of High-Throughput Whole Slide Imaging in Digital Pathology
Mahdi S. Hosseini, Yueyang Zhang, Lyndon Chan +3
One of the challenges facing the adoption of digital pathology workflows for clinical use is the need for automated quality control. As the scanners sometimes determine focus inacc…