11 citations · 35 across the 11 of their papers we have counts for
14 papers
Censor-aware Semi-supervised Learning for Survival Time Prediction from Medical Images
Renato Hermoza, Gabriel Maicas, Jacinto C. Nascimento +1
Survival time prediction from medical images is important for treatment planning, where accurate estimations can improve healthcare quality. One issue affecting the training of sur…
Mutual information neural estimation for unsupervised multi-modal registration of brain images
Gerard Snaauw, Michele Sasdelli, Gabriel Maicas +4
Many applications in image-guided surgery and therapy require fast and reliable non-linear, multi-modal image registration. Recently proposed unsupervised deep learning-based regis…
Post-hoc Overall Survival Time Prediction from Brain MRI
Renato Hermoza, Gabriel Maicas, Jacinto C. Nascimento +1
Overall survival (OS) time prediction is one of the most common estimates of the prognosis of gliomas and is used to design an appropriate treatment planning. State-of-the-art (SOT…
Detecting, Localising and Classifying Polyps from Colonoscopy Videos using Deep Learning
Yu Tian, Leonardo Zorron Cheng Tao Pu, Yuyuan Liu +6
In this paper, we propose and analyse a system that can automatically detect, localise and classify polyps from colonoscopy videos. The detection of frames with polyps is formulate…
Self-supervised Depth Estimation to Regularise Semantic Segmentation in Knee Arthroscopy
Fengbei Liu, Yaqub Jonmohamadi, Gabriel Maicas +2
Intra-operative automatic semantic segmentation of knee joint structures can assist surgeons during knee arthroscopy in terms of situational awareness. However, due to poor imaging…
Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy
Yu Tian, Gabriel Maicas, Leonardo Zorron Cheng Tao Pu +3
Anomaly detection methods generally target the learning of a normal image distribution (i.e., inliers showing healthy cases) and during testing, samples relatively far from the lea…
Region Proposals for Saliency Map Refinement for Weakly-supervised Disease Localisation and Classification
Renato Hermoza, Gabriel Maicas, Jacinto C. Nascimento +1
The deployment of automated systems to diagnose diseases from medical images is challenged by the requirement to localise the diagnosed diseases to justify or explain the classific…
Semi-supervised Multi-domain Multi-task Training for Metastatic Colon Lymph Node Diagnosis From Abdominal CT
Saskia Glaser, Gabriel Maicas, Sergei Bedrikovetski +2
The diagnosis of the presence of metastatic lymph nodes from abdominal computed tomography (CT) scans is an essential task performed by radiologists to guide radiation and chemothe…
Unsupervised Dual Adversarial Learning for Anomaly Detection in Colonoscopy Video Frames
Yuyuan Liu, Yu Tian, Gabriel Maicas +4
The automatic detection of frames containing polyps from a colonoscopy video sequence is an important first step for a fully automated colonoscopy analysis tool. Typically, such de…
Unsupervised Task Design to Meta-Train Medical Image Classifiers
Gabriel Maicas, Cuong Nguyen, Farbod Motlagh +2
Meta-training has been empirically demonstrated to be the most effective pre-training method for few-shot learning of medical image classifiers (i.e., classifiers modeled with smal…
End-to-End Diagnosis and Segmentation Learning from Cardiac Magnetic Resonance Imaging
Gerard Snaauw, Dong Gong, Gabriel Maicas +4
Cardiac magnetic resonance (CMR) is used extensively in the diagnosis and management of cardiovascular disease. Deep learning methods have proven to deliver segmentation results co…
Pre and Post-hoc Diagnosis and Interpretation of Malignancy from Breast DCE-MRI
Gabriel Maicas, Andrew P. Bradley, Jacinto C. Nascimento +2
We propose a new method for breast cancer screening from DCE-MRI based on a post-hoc approach that is trained using weakly annotated data (i.e., labels are available only at the im…
Model Agnostic Saliency for Weakly Supervised Lesion Detection from Breast DCE-MRI
Gabriel Maicas, Gerard Snaauw, Andrew P. Bradley +2
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 interpr…
Training Medical Image Analysis Systems like Radiologists
Gabriel Maicas, Andrew P. Bradley, Jacinto C. Nascimento +2
The training of medical image analysis systems using machine learning approaches follows a common script: collect and annotate a large dataset, train the classifier on the training…