60 citations · 63 across the 4 of their papers we have counts for
9 papers
Deep neural networks with controlled variable selection for the identification of putative causal genetic variants
Peyman H. Kassani, Fred Lu, Yann Le Guen +1
Deep neural networks (DNN) have been used successfully in many scientific problems for their high prediction accuracy, but their application to genetic studies remains challenging…
Automatic Polyp Segmentation Using Convolutional Neural Networks
Sara Hosseinzadeh Kassani, Peyman Hosseinzadeh Kassani, Michal J. Wesolowski +2
Colorectal cancer is the third most common cancer-related death after lung cancer and breast cancer worldwide. The risk of developing colorectal cancer could be reduced by early di…
Classification of Histopathological Biopsy Images Using Ensemble of Deep Learning Networks
Sara Hosseinzadeh Kassani, Peyman Hosseinzadeh Kassani, Michal J. Wesolowski +2
Breast cancer is one of the leading causes of death across the world in women. Early diagnosis of this type of cancer is critical for treatment and patient care. Computer-aided det…
A Hybrid Deep Learning Architecture for Leukemic B-lymphoblast Classification
Sara Hosseinzadeh Kassani, Peyman Hosseinzadeh kassani, Michal J. Wesolowski +2
Automatic detection of leukemic B-lymphoblast cancer in microscopic images is very challenging due to the complicated nature of histopathological structures. To tackle this issue,…
Breast Cancer Diagnosis with Transfer Learning and Global Pooling
Sara Hosseinzadeh Kassani, Peyman Hosseinzadeh Kassani, Michal J. Wesolowski +2
Breast cancer is one of the most common causes of cancer-related death in women worldwide. Early and accurate diagnosis of breast cancer may significantly increase the survival rat…
k-Relevance Vectors: Considering Relevancy Beside Nearness
Sara Hosseinzadeh Kassani, Farhood Rismanchian, Peyman Hosseinzadeh Kassani
This study combines two different learning paradigms, k-nearest neighbor (k-NN) rule, as memory-based learning paradigm and relevance vector machines (RVM), as statistical learning…