25 citations · 111 across the 15 of their papers we have counts for
10 papers · 1 filter
Learning Binary and Sparse Permutation-Invariant Representations for Fast and Memory Efficient Whole Slide Image Search
Sobhan Hemati, Shivam Kalra, Morteza Babaie +1
Learning suitable Whole slide images (WSIs) representations for efficient retrieval systems is a non-trivial task. The WSI embeddings obtained from current methods are in Euclidean…
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…
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…
Subtractive Perceptrons for Learning Images: A Preliminary Report
H. R. Tizhoosh, Shivam Kalra, Shalev Lifshitz +1
In recent years, artificial neural networks have achieved tremendous success for many vision-based tasks. However, this success remains within the paradigm of \emph{weak AI} where…
Projectron -- A Shallow and Interpretable Network for Classifying Medical Images
Aditya Sriram, Shivam Kalra, H. R. Tizhoosh
This paper introduces the `Projectron' as a new neural network architecture that uses Radon projections to both classify and represent medical images. The motivation is to build sh…
Convolutional Neural Networks for Histopathology Image Classification: Training vs. Using Pre-Trained Networks
Brady Kieffer, Morteza Babaie, Shivam Kalra +1
We explore the problem of classification within a medical image data-set based on a feature vector extracted from the deepest layer of pre-trained Convolution Neural Networks. We h…