Search Results for author:
Found 11 papers, 1 papers with code
Automatic lesion detection, segmentation and characterization via 3D multiscale morphological sifting in breast MRI
Breast lesions are firstly extracted as region candidates using the novel 3D multiscale morphological sifting (MMS).
Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and Mask R-CNN
We propose a fully-integrated computer-aided detection (CAD) system for simultaneous mammographic mass detection and segmentation without user intervention.
Pre and Post-hoc Diagnosis and Interpretation of Malignancy from Breast DCE-MRI
Conversely, traditional approaches follow a pre-hoc approach that initially localises suspicious areas that are subsequently classified to establish the breast malignancy -- this approach is trained using strongly annotated data (i. e., it needs a delineation and classification of all lesions in an image).
Model Agnostic Saliency for Weakly Supervised Lesion Detection from Breast DCE-MRI
There is a heated debate on how to interpret the decisions provided by deep learning models (DLM), where the main approaches rely on the visualization of salient regions to interpret the DLM classification process.
Why rankings of biomedical image analysis competitions should be interpreted with care
International challenges have become the standard for validation of biomedical image analysis methods.
Producing radiologist-quality reports for interpretable artificial intelligence
Current approaches to explaining the decisions of deep learning systems for medical tasks have focused on visualising the elements that have contributed to each decision.
Training Medical Image Analysis Systems like Radiologists
This process bears no direct resemblance with radiologist training, which is based on solving a series of tasks of increasing difficulty, where each task involves the use of significantly smaller datasets than those used in machine learning.
Detecting hip fractures with radiologist-level performance using deep neural networks
We developed an automated deep learning system to detect hip fractures from frontal pelvic x-rays, an important and common radiological task.
Automated Detection of Individual Micro-calcifications from Mammograms using a Multi-stage Cascade Approach
In mammography, the efficacy of computer-aided detection methods depends, in part, on the robust localisation of micro-calcifications ($\mu$C).
Automated 5-year Mortality Prediction using Deep Learning and Radiomics Features from Chest Computed Tomography
We propose new methods for the prediction of 5-year mortality in elderly individuals using chest computed tomography (CT).
Deep Structured learning for mass segmentation from Mammograms
In this paper, we present a novel method for the segmentation of breast masses from mammograms exploring structured and deep learning.
