139 citations · 787 across the 16 of their papers we have counts for
16 papers
Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions
Abhijit Guha Roy, Jie Ren, Shekoofeh Azizi +18
We develop and rigorously evaluate a deep learning based system that can accurately classify skin conditions while detecting rare conditions for which there is not enough data avai…
Deep Structural Causal Models for Tractable Counterfactual Inference
Nick Pawlowski, Daniel C. Castro, Ben Glocker
We formulate a general framework for building structural causal models (SCMs) with deep learning components. The proposed approach employs normalising flows and variational inferen…
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty
Miguel Monteiro, Loïc Le Folgoc, Daniel Coelho de Castro +5
In image segmentation, there is often more than one plausible solution for a given input. In medical imaging, for example, experts will often disagree about the exact location of o…
An Explicit Local and Global Representation Disentanglement Framework with Applications in Deep Clustering and Unsupervised Object Detection
Rujikorn Charakorn, Yuttapong Thawornwattana, Sirawaj Itthipuripat +3
Visual data can be understood at different levels of granularity, where global features correspond to semantic-level information and local features correspond to texture patterns.…
Representation Disentanglement for Multi-task Learning with application to Fetal Ultrasound
Qingjie Meng, Nick Pawlowski, Daniel Rueckert +1
One of the biggest challenges for deep learning algorithms in medical image analysis is the indiscriminate mixing of image properties, e.g. artifacts and anatomy. These entangled i…
Needles in Haystacks: On Classifying Tiny Objects in Large Images
Nick Pawlowski, Suvrat Bhooshan, Nicolas Ballas +3
In some important computer vision domains, such as medical or hyperspectral imaging, we care about the classification of tiny objects in large images. However, most Convolutional N…