1.1k citations · 2.7k across the 92 of their papers we have counts for
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Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification
Marc Combalia, Veronica Vilaplana
We propose a patch sampling strategy based on a sequential Monte-Carlo method for high resolution image classification in the context of Multiple Instance Learning. When compared w…
Probabilistic Object Detection: Definition and Evaluation
David Hall, Feras Dayoub, John Skinner +6
We introduce Probabilistic Object Detection, the task of detecting objects in images and accurately quantifying the spatial and semantic uncertainties of the detections. Given the…
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…
Approximate Fisher Information Matrix to Characterise the Training of Deep Neural Networks
Zhibin Liao, Tom Drummond, Ian Reid +1
In this paper, we introduce a novel methodology for characterising the performance of deep learning networks (ResNets and DenseNet) with respect to training convergence and general…
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…
Multi-modal Cycle-consistent Generalized Zero-Shot Learning
Rafael Felix, B. G. Vijay Kumar, Ian Reid +1
In generalized zero shot learning (GZSL), the set of classes are split into seen and unseen classes, where training relies on the semantic features of the seen and unseen classes a…
Bayesian Semantic Instance Segmentation in Open Set World
Trung Pham, Vijay Kumar B G, Thanh-Toan Do +2
This paper addresses the semantic instance segmentation task in the open-set conditions, where input images can contain known and unknown object classes. The training process of ex…
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…
Producing radiologist-quality reports for interpretable artificial intelligence
William Gale, Luke Oakden-Rayner, Gustavo Carneiro +2
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. We argue…
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…