This system optimally integrates information from any combination of the above data sources, so as to improve the efficiency, sensitivity and specificity of breast cancer diagnosis. The project is a collaboration between Instituto Superior Técnico, University of Adelaide and University of Queensland. Also, the BreastScreening project receives contributions from both MIDA and MIMBCD-UI projects.

Project Description

With the BreastScreening project we have two research questions: 1) can we use Deep ConvNet models pre-trained from Computer Vision (CV) datasets on medical image analysis applications; and 2) can we use Deep ConvNet to analyse unregistered medical images.

We present a novel methodology for the automated detection of breast lesions from Dynamic Contrast-Enhanced Magnetic Resonance (DCE-MRI) volumes. Our method, based on Deep Reinforcement Learning (DRL), significantly reduces the inference time for lesion detection compared to an exhaustive search, while retaining state-of-art accuracy.

In short, the BreastScreening project is an automated analysis of Multi-Modal Medical Data using Deep Belief Networks (DBN). This project is an ARC Discovery Project (DP140102794) aiming at: (i) automatically detect and segment suspicious regions from different breast imaging data, e.g., MG, US and MRI; (ii) estimate BI-RADS scores (from 0 to 6) from the segmentations and the patient’s clinical records; (iii) retrieve similar cases from our database, given the above estimation results; and (iv) automatically extract relevant features from any combination of input data types.


Our Researchers and Fellows:

Gabriel Maicas

ARC Research Fellow

Gustavo Carneiro

Professor (Coordinator)

Jacinto Nascimento

Assistant Professor

Support Researchers

Our Support Researchers, Fellows and Interns:

Carlos Santiago

Junior Researcher

Medical Collaboration

Our Doctors, Physicians and Clinicians:

Ana Germano

Hospital Fernando Fonseca

Clara Aleluia

Hospital Fernando Fonseca

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