Pan-Cancer Diagnostic Consensus Through Searching Archival Histopathology Images Using Artificial Intelligence

by   Shivam Kalra, et al.

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 pathologists with diagnostic tasks. Whereas machine learning involving classification and segmentation methods have obvious benefits for image analysis in pathology, image search represents a fundamental shift in computational pathology. Matching the pathology of new patients with already diagnosed and curated cases offers pathologist a novel approach to improve diagnostic accuracy through visual inspection of similar cases and computational majority vote for consensus building. In this study, we report the results from searching the largest public repository (The Cancer Genome Atlas [TCGA] program by National Cancer Institute, USA) of whole slide images from almost 11,000 patients depicting different types of malignancies. For the first time, we successfully indexed and searched almost 30,000 high-resolution digitized slides constituting 16 terabytes of data comprised of 20 million 1000x1000 pixels image patches. The TCGA image database covers 25 anatomic sites and contains 32 cancer subtypes. High-performance storage and GPU power were employed for experimentation. The results were assessed with conservative "majority voting" to build consensus for subtype diagnosis through vertical search and demonstrated high accuracy values for both frozen sections slides (e.g., bladder urothelial carcinoma 93 and ovarian serous cystadenocarcinoma 99 (e.g., prostate adenocarcinoma 98 100 consensus appears to be possible for rendering diagnoses if a sufficiently large number of searchable cases are available for each cancer subtype.


page 1

page 12

page 13

page 17

page 20

page 21


Yottixel – An Image Search Engine for Large Archives of Histopathology Whole Slide Images

With the emergence of digital pathology, searching for similar images in...

Towards More Transparent and Accurate Cancer Diagnosis with an Unsupervised CAE Approach

Digital pathology has revolutionized cancer diagnosis by leveraging Cont...

BRACS: A Dataset for BReAst Carcinoma Subtyping in H E Histology Images

Breast cancer is the most commonly diagnosed cancer and registers the hi...

Advances of Artificial Intelligence in Classical and Novel Spectroscopy-Based Approaches for Cancer Diagnostics. A Review

Cancer is one of the leading causes of death worldwide. Fast and safe ea...

Gastric Cancer Detection from X-ray Images Using Effective Data Augmentation and Hard Boundary Box Training

X-ray examination is suitable for screening of gastric cancer. Compared ...

Artificial intelligence technology in oncology: a new technological paradigm

Artificial Intelligence (AI) technology is based on theory and developme...

Similar Image Search for Histopathology: SMILY

The increasing availability of large institutional and public histopatho...

Please sign up or login with your details

Forgot password? Click here to reset