160 citations · 607 across the 73 of their papers we have counts for
42 papers · 1 filter
ALFA -- Leveraging All Levels of Feature Abstraction for Enhancing the Generalization of Histopathology Image Classification Across Unseen Hospitals
Milad Sikaroudi, Maryam Hosseini, Shahryar Rahnamayan +1
We propose an exhaustive methodology that leverages all levels of feature abstraction, targeting an enhancement in the generalizability of image classification to unobserved hospit…
Evolutionary Computation in Action: Feature Selection for Deep Embedding Spaces of Gigapixel Pathology Images
Azam Asilian Bidgoli, Shahryar Rahnamayan, Taher Dehkharghanian +2
One of the main obstacles of adopting digital pathology is the challenge of efficient processing of hyperdimensional digitized biopsy samples, called whole slide images (WSIs). Exp…
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…
LILE: Look In-Depth before Looking Elsewhere -- A Dual Attention Network using Transformers for Cross-Modal Information Retrieval in Histopathology Archives
Danial Maleki, H. R Tizhoosh
The volume of available data has grown dramatically in recent years in many applications. Furthermore, the age of networks that used multiple modalities separately has practically…
Beyond Neighbourhood-Preserving Transformations for Quantization-Based Unsupervised Hashing
Sobhan Hemati, H. R. Tizhoosh
An effective unsupervised hashing algorithm leads to compact binary codes preserving the neighborhood structure of data as much as possible. One of the most established schemes for…
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…
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…
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…
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…
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…
Facial Recognition with Encoded Local Projections
Dhruv Sharma, Sarim Zafar, Morteza Babaie +1
Encoded Local Projections (ELP) is a recently introduced dense sampling image descriptor which uses projections in small neighbourhoods to construct a histogram/descriptor for the…
A Dataset and Preliminary Results for Umpire Pose Detection Using SVM Classification of Deep Features
Aravind Ravi, Harshwin Venugopal, Sruthy Paul +1
In recent years, there has been increased interest in video summarization and automatic sports highlights generation. In this work, we introduce a new dataset, called SNOW, for ump…
The Effects of Image Pre- and Post-Processing, Wavelet Decomposition, and Local Binary Patterns on U-Nets for Skin Lesion Segmentation
Sara Ross-Howe, H. R. Tizhoosh
Skin cancer is a widespread, global, and potentially deadly disease, which over the last three decades has afflicted more lives in the USA than all other forms of cancer combined.…
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…
Local Radon Descriptors for Image Search
Morteza Babaie, H. R. Tizhoosh, Amin Khatami +1
Radon transform and its inverse operation are important techniques in medical imaging tasks. Recently, there has been renewed interest in Radon transform for applications such as c…
Fast Barcode Retrieval for Consensus Contouring
H. R. Tizhoosh, G. J. Czarnota
Marking tumors and organs is a challenging task suffering from both inter- and intra-observer variability. The literature quantifies observer variability by generating consensus am…
A Comparative Study of CNN, BoVW and LBP for Classification of Histopathological Images
Meghana Dinesh Kumar, Morteza Babaie, Shujin Zhu +2
Despite the progress made in the field of medical imaging, it remains a large area of open research, especially due to the variety of imaging modalities and disease-specific charac…
Skin Lesion Segmentation: U-Nets versus Clustering
Bill S. Lin, Kevin Michael, Shivam Kalra +1
Many automatic skin lesion diagnosis systems use segmentation as a preprocessing step to diagnose skin conditions because skin lesion shape, border irregularity, and size can influ…
Learning Autoencoded Radon Projections
Aditya Sriram, Shivam Kalra, H. R. Tizhoosh +1
Autoencoders have been recently used for encoding medical images. In this study, we design and validate a new framework for retrieving medical images by classifying Radon projectio…
Combining Real-Valued and Binary Gabor-Radon Features for Classification and Search in Medical Imaging Archives
Hamed Erfankhah, Mehran Yazdi, H. R. Tizhoosh
Content-based image retrieval (CBIR) of medical images in large datasets to identify similar images when a query image is given can be very useful in improving the diagnostic decis…
Classification and Retrieval of Digital Pathology Scans: A New Dataset
Morteza Babaie, Shivam Kalra, Aditya Sriram +5
In this paper, we introduce a new dataset, \textbf{Kimia Path24}, for image classification and retrieval in digital pathology. We use the whole scan images of 24 different tissue t…