160 citations · 607 across the 73 of their papers we have counts for
14 papers · 1 filter
A non-alternating graph hashing algorithm for large scale image search
Sobhan Hemati, Mohammad Hadi Mehdizavareh, Shojaeddin Chenouri +1
In the era of big data, methods for improving memory and computational efficiency have become crucial for successful deployment of technologies. Hashing is one of the most effectiv…
Ink Marker Segmentation in Histopathology Images Using Deep Learning
Danial Maleki, Mehdi Afshari, Morteza Babaie +1
Due to the recent advancements in machine vision, digital pathology has gained significant attention. Histopathology images are distinctly rich in visual information. The tissue gl…
Forming Local Intersections of Projections for Classifying and Searching Histopathology Images
Aditya Sriram, Shivam Kalra, Morteza Babaie +5
In this paper, we propose a novel image descriptor called Forming Local Intersections of Projections (FLIP) and its multi-resolution version (mFLIP) for representing histopathology…
A new Local Radon Descriptor for Content-Based Image Search
Morteza Babaie, Hany Kashani, Meghana D. Kumar +1
Content-based image retrieval (CBIR) is an essential part of computer vision research, especially in medical expert systems. Having a discriminative image descriptor with the least…
Searching for Pneumothorax in Half a Million Chest X-Ray Images
Antonio Sze-To, Hamid Tizhoosh
Pneumothorax, a collapsed or dropped lung, is a fatal condition typically detected on a chest X-ray by an experienced radiologist. Due to shortage of such experts, automated detect…
Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating Theorem
Milad Sikaroudi, Benyamin Ghojogh, Fakhri Karray +2
Variants of Triplet networks are robust entities for learning a discriminative embedding subspace. There exist different triplet mining approaches for selecting the most suitable t…
Offline versus Online Triplet Mining based on Extreme Distances of Histopathology Patches
Milad Sikaroudi, Benyamin Ghojogh, Amir Safarpoor +3
We analyze the effect of offline and online triplet mining for colorectal cancer (CRC) histopathology dataset containing 100,000 patches. We consider the extreme, i.e., farthest an…
A Comparative Study of U-Net Topologies for Background Removal in Histopathology Images
Abtin Riasatian, Maral Rasoolijaberi, Morteza Babaei +1
During the last decade, the digitization of pathology has gained considerable momentum. Digital pathology offers many advantages including more efficient workflows, easier collabor…
Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study
Milad Sikaroudi, Amir Safarpoor, Benyamin Ghojogh +3
As many algorithms depend on a suitable representation of data, learning unique features is considered a crucial task. Although supervised techniques using deep neural networks hav…
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…
Representation Learning of Histopathology Images using Graph Neural Networks
Mohammed Adnan, Shivam Kalra, Hamid R. Tizhoosh
Representation learning for Whole Slide Images (WSIs) is pivotal in developing image-based systems to achieve higher precision in diagnostic pathology. We propose a two-stage frame…
Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks
Benyamin Ghojogh, Milad Sikaroudi, Sobhan Shafiei +3
Siamese neural network is a very powerful architecture for both feature extraction and metric learning. It usually consists of several networks that share weights. The Siamese conc…
Weighted Fisher Discriminant Analysis in the Input and Feature Spaces
Benyamin Ghojogh, Milad Sikaroudi, H. R. Tizhoosh +2
Fisher Discriminant Analysis (FDA) is a subspace learning method which minimizes and maximizes the intra- and inter-class scatters of data, respectively. Although, in FDA, all the…
Studying the Effect of Digital Stain Separation of Histopathology Images on Image Search Performance
Alison K. Cheeseman, Hamid R. Tizhoosh, Edward R. Vrscay
Due to recent advances in technology, digitized histopathology images are now widely available for both clinical and research purposes. Accordingly, research into computerized imag…