160 citations · 607 across the 73 of their papers we have counts for
84 papers
Training Artificial Neural Networks by Coordinate Search Algorithm
Ehsan Rokhsatyazdi, Shahryar Rahnamayan, Sevil Zanjani Miyandoab +2
Training Artificial Neural Networks poses a challenging and critical problem in machine learning. Despite the effectiveness of gradient-based learning methods, such as Stochastic G…
A Preliminary Investigation into Search and Matching for Tumour Discrimination in WHO Breast Taxonomy Using Deep Networks
Abubakr Shafique, Ricardo Gonzalez, Liron Pantanowitz +4
Breast cancer is one of the most common cancers affecting women worldwide. They include a group of malignant neoplasms with a variety of biological, clinical, and histopathological…
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…
An Investigation into Glomeruli Detection in Kidney H&E and PAS Images using YOLO
Kimia Hemmatirad, Morteza Babaie, Jeffrey Hodgin +2
Context: Analyzing digital pathology images is necessary to draw diagnostic conclusions by investigating tissue patterns and cellular morphology. However, manual evaluation can be…
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…
Ranking Loss and Sequestering Learning for Reducing Image Search Bias in Histopathology
Pooria Mazaheri, Azam Asilian Bidgoli, Shahryar Rahnamayan +1
Recently, deep learning has started to play an essential role in healthcare applications, including image search in digital pathology. Despite the recent progress in computer visio…
Comments on 'Fast and scalable search of whole-slide images via self-supervised deep learning'
Milad Sikaroudi, Mehdi Afshari, Abubakr Shafique +2
Chen et al. [Chen2022] recently published the article 'Fast and scalable search of whole-slide images via self-supervised deep learning' in Nature Biomedical Engineering. The autho…
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…
Cluster Based Secure Multi-Party Computation in Federated Learning for Histopathology Images
S. Maryam Hosseini, Milad Sikaroudi, Morteza Babaei +1
Federated learning (FL) is a decentralized method enabling hospitals to collaboratively learn a model without sharing private patient data for training. In FL, participant hospital…
Monitoring Shortcut Learning using Mutual Information
Mohammed Adnan, Yani Ioannou, Chuan-Yung Tsai +3
The failure of deep neural networks to generalize to out-of-distribution data is a well-known problem and raises concerns about the deployment of trained networks in safety-critica…
Hospital-Agnostic Image Representation Learning in Digital Pathology
Milad Sikaroudi, Shahryar Rahnamayan, H. R. Tizhoosh
Whole Slide Images (WSIs) in digital pathology are used to diagnose cancer subtypes. The difference in procedures to acquire WSIs at various trial sites gives rise to variability i…
Learning to Predict RNA Sequence Expressions from Whole Slide Images with Applications for Search and Classification
Amir Safarpoor, Jason D. Hipp, H. R. Tizhoosh
Deep learning methods are widely applied in digital pathology to address clinical challenges such as prognosis and diagnosis. As one of the most recent applications, deep models ha…
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…
Decentralized Federated Learning through Proxy Model Sharing
Shivam Kalra, Junfeng Wen, Jesse C. Cresswell +2
Institutions in highly regulated domains such as finance and healthcare often have restrictive rules around data sharing. Federated learning is a distributed learning framework tha…
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…
Unsupervised Detection of Lung Nodules in Chest Radiography Using Generative Adversarial Networks
Nitish Bhatt, David Ramon Prados, Nedim Hodzic +2
Lung nodules are commonly missed in chest radiographs. We propose and evaluate P-AnoGAN, an unsupervised anomaly detection approach for lung nodules in radiographs. P-AnoGAN modifi…
Automatic Multi-Stain Registration of Whole Slide Images in Histopathology
Abubakr Shafique, Morteza Babaie, Mahjabin Sajadi +3
Joint analysis of multiple biomarker images and tissue morphology is important for disease diagnosis, treatment planning and drug development. It requires cross-staining comparison…
A Similarity Measure of Histopathology Images by Deep Embeddings
Mehdi Afshari, H. R. Tizhoosh
Histopathology digital scans are large-size images that contain valuable information at the pixel level. Content-based comparison of these images is a challenging task. This study…
Histogram of Cell Types: Deep Learning for Automated Bone Marrow Cytology
Rohollah Moosavi Tayebi, Youqing Mu, Taher Dehkharghanian +5
Bone marrow cytology is required to make a hematological diagnosis, influencing critical clinical decision points in hematology. However, bone marrow cytology is tedious, limited t…
Pay Attention with Focus: A Novel Learning Scheme for Classification of Whole Slide Images
Shivam Kalra, Mohammed Adnan, Sobhan Hemati +3
Deep learning methods such as convolutional neural networks (CNNs) are difficult to directly utilize to analyze whole slide images (WSIs) due to the large image dimensions. We over…
Colored Kimia Path24 Dataset: Configurations and Benchmarks with Deep Embeddings
Sobhan Shafiei, Morteza Babaie, Shivam Kalra +1
The Kimia Path24 dataset has been introduced as a classification and retrieval dataset for digital pathology. Although it provides multi-class data, the color information has been…
Searching for Pneumothorax in X-Ray Images Using Autoencoded Deep Features
Antonio Sze-To, Abtin Riasatian, Hamid R. Tizhoosh
Fast diagnosis and treatment of pneumothorax, a collapsed or dropped lung, is crucial to avoid fatalities. Pneumothorax is typically detected on a chest X-ray image through visual…
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…
Magnification Generalization for Histopathology Image Embedding
Milad Sikaroudi, Benyamin Ghojogh, Fakhri Karray +2
Histopathology image embedding is an active research area in computer vision. Most of the embedding models exclusively concentrate on a specific magnification level. However, a use…