4 papers
A Deep Bayesian Convolutional Spiking Neural Network-based CAD system with Uncertainty Quantification for Medical Images Classification
Mohaddeseh Chegini, Ali Mahloojifar
The Computer_Aided Diagnosis (CAD) systems facilitate accurate diagnosis of diseases. The development of CADs by leveraging third generation neural network, namely, Spiking Neural…
BI-RADS prediction of mammographic masses using uncertainty information extracted from a Bayesian Deep Learning model
Mohaddeseh Chegini, Ali Mahloojifar
The BI_RADS score is a probabilistic reporting tool used by radiologists to express the level of uncertainty in predicting breast cancer based on some morphological features in mam…
Reliable Breast Cancer Molecular Subtype Prediction based on uncertainty-aware Bayesian Deep Learning by Mammography
Mohaddeseh Chegini, Ali Mahloojifar
Breast cancer is a heterogeneous disease with different molecular subtypes, clinical behavior, treatment responses as well as survival outcomes. The development of a reliable, accu…
Modifying the U-Net's Encoder-Decoder Architecture for Segmentation of Tumors in Breast Ultrasound Images
Sina Derakhshandeh, Ali Mahloojifar
Segmentation is one of the most significant steps in image processing. Segmenting an image is a technique that makes it possible to separate a digital image into various areas base…