activity
20182020
most citedClassification of Histopathological Biopsy Images Using Ensemble of Deep Learning Networks

60 citations · 66 across the 4 of their papers we have counts for

collaborators

10 papers

cs.CR20204 cited

Access Control Management for Computer-Aided Diagnosis Systems using Blockchain

Mayra Samaniego, Sara Hosseinzadeh Kassani, Cristian Espana +1

Computer-Aided Diagnosis (CAD) systems have emerged to support clinicians in interpreting medical images. CAD systems are traditionally combined with artificial intelligence (AI),…

eess.IV2020

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…

eess.IV201960 cited

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…

eess.IV20192 cited

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,…

eess.IV2019

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…

cs.LG2019

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…