Search Results for author:
Found 14 papers, 4 papers with code
Censor-aware Semi-supervised Learning for Survival Time Prediction from Medical Images
In this work, we propose a new training method that predicts survival time using all censored and uncensored data.
Mutual information neural estimation for unsupervised multi-modal registration of brain images
We propose guiding the training of a deep learning-based registration method with MI estimation between an image-pair in an end-to-end trainable network.
Post-hoc Overall Survival Time Prediction from Brain MRI
In this paper, we introduce a new post-hoc method for OS time prediction that does not require segmentation map annotation for training.
Detecting, Localising and Classifying Polyps from Colonoscopy Videos using Deep Learning
In this paper, we propose and analyse a system that can automatically detect, localise and classify polyps from colonoscopy videos.
Self-supervised Depth Estimation to Regularise Semantic Segmentation in Knee Arthroscopy
In this paper, we propose a novel self-supervised monocular depth estimation to regularise the training of the semantic segmentation in knee arthroscopy.
Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy
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 learned distribution are classified as anomalies (i. e., outliers showing disease cases).
Region Proposals for Saliency Map Refinement for Weakly-supervised Disease Localisation and Classification
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 classification decision.
Semi-supervised Multi-domain Multi-task Training for Metastatic Colon Lymph Node Diagnosis From Abdominal CT
However, the lack of annotations for the localisation of the regions of interest (ROIs) containing lymph nodes can limit classification accuracy due to the small size of the relevant ROIs in this problem.
Unsupervised Dual Adversarial Learning for Anomaly Detection in Colonoscopy Video Frames
We show that our proposed approach achieves the state-of-the-art result on this data set, compared with recently proposed anomaly detection systems.
Unsupervised Task Design to Meta-Train Medical Image Classifiers
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 small training sets).
End-to-End Diagnosis and Segmentation Learning from Cardiac Magnetic Resonance Imaging
In this paper, we propose a learning method to train diagnosis models, where our approach is designed to work with relatively small datasets.
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.
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.
