1 code implementation • 26 May 2022 • Renato Hermoza, Gabriel Maicas, Jacinto C. Nascimento, Gustavo Carneiro
In this work, we propose a new training method that predicts survival time using all censored and uncensored data.
no code implementations • 25 Jan 2022 • Gerard Snaauw, Michele Sasdelli, Gabriel Maicas, Stephan Lau, Johan Verjans, Mark Jenkinson, Gustavo Carneiro
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.
1 code implementation • 22 Feb 2021 • Renato Hermoza, Gabriel Maicas, Jacinto C. Nascimento, Gustavo Carneiro
In this paper, we introduce a new post-hoc method for OS time prediction that does not require segmentation map annotation for training.
no code implementations • 9 Jan 2021 • Yu Tian, Leonardo Zorron Cheng Tao Pu, Yuyuan Liu, Gabriel Maicas, Johan W. Verjans, Alastair D. Burt, Seon Ho Shin, Rajvinder Singh, Gustavo Carneiro
In this paper, we propose and analyse a system that can automatically detect, localise and classify polyps from colonoscopy videos.
no code implementations • 5 Jul 2020 • Fengbei Liu, Yaqub Jonmohamadi, Gabriel Maicas, Ajay K. Pandey, Gustavo Carneiro
In this paper, we propose a novel self-supervised monocular depth estimation to regularise the training of the semantic segmentation in knee arthroscopy.
1 code implementation • 26 Jun 2020 • Yu Tian, Gabriel Maicas, Leonardo Zorron Cheng Tao Pu, Rajvinder Singh, Johan W. Verjans, Gustavo Carneiro
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).
1 code implementation • 21 May 2020 • Renato Hermoza, Gabriel Maicas, Jacinto C. Nascimento, Gustavo Carneiro
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.
no code implementations • 23 Oct 2019 • Saskia Glaser, Gabriel Maicas, Sergei Bedrikovetski, Tarik Sammour, Gustavo Carneiro
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.
no code implementations • 23 Oct 2019 • Yuyuan Liu, Yu Tian, Gabriel Maicas, Leonardo Z. C. T. Pu, Rajvinder Singh, Johan W. Verjans, Gustavo Carneiro
We show that our proposed approach achieves the state-of-the-art result on this data set, compared with recently proposed anomaly detection systems.
no code implementations • 17 Jul 2019 • Gabriel Maicas, Cuong Nguyen, Farbod Motlagh, Jacinto C. Nascimento, Gustavo Carneiro
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).
no code implementations • 23 Oct 2018 • Gerard Snaauw, Dong Gong, Gabriel Maicas, Anton Van Den Hengel, Wiro J. Niessen, Johan Verjans, Gustavo Carneiro
In this paper, we propose a learning method to train diagnosis models, where our approach is designed to work with relatively small datasets.
no code implementations • 25 Sep 2018 • Gabriel Maicas, Andrew P. Bradley, Jacinto C. Nascimento, Ian Reid, Gustavo Carneiro
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).
no code implementations • 20 Jul 2018 • Gabriel Maicas, Gerard Snaauw, Andrew P. Bradley, Ian Reid, Gustavo Carneiro
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.
no code implementations • 28 May 2018 • Gabriel Maicas, Andrew P. Bradley, Jacinto C. Nascimento, Ian Reid, Gustavo Carneiro
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.