Timezone: »
Many real-world vision problems suffer from inherent ambiguities. In clinical applications for example, it might not be clear from a CT scan alone which particular region is cancer tissue. Therefore a group of graders typically produces a set of diverse but plausible segmentations. We consider the task of learning a distribution over segmentations given an input. To this end we propose a generative segmentation model based on a combination of a U-Net with a conditional variational autoencoder that is capable of efficiently producing an unlimited number of plausible hypotheses. We show on a lung abnormalities segmentation task and on a Cityscapes segmentation task that our model reproduces the possible segmentation variants as well as the frequencies with which they occur, doing so significantly better than published approaches. These models could have a high impact in real-world applications, such as being used as clinical decision-making algorithms accounting for multiple plausible semantic segmentation hypotheses to provide possible diagnoses and recommend further actions to resolve the present ambiguities.
Author Information
Simon Kohl (German Cancer Research Center (DKFZ))
Bernardino Romera-Paredes (DeepMind)
Clemens Meyer (DeepMind)
Jeffrey De Fauw (DeepMind)
Joseph R. Ledsam (DeepMind)
Klaus Maier-Hein (German Cancer Research Center)
Ali Eslami (DeepMind)
Danilo Jimenez Rezende (Google DeepMind)
Olaf Ronneberger (DeepMind)
Related Events (a corresponding poster, oral, or spotlight)
-
2018 Poster: A Probabilistic U-Net for Segmentation of Ambiguous Images »
Wed Dec 5th 03:45 -- 05:45 PM Room Room 517 AB
More from the Same Authors
-
2020 Poster: Self-Supervised MultiModal Versatile Networks »
Jean-Baptiste Alayrac · Adria Recasens · Rosalia Schneider · Relja Arandjelović · Jason Ramapuram · Jeffrey De Fauw · Lucas Smaira · Sander Dieleman · Andrew Zisserman -
2019 Poster: Towards Interpretable Reinforcement Learning Using Attention Augmented Agents »
Alexander Mott · Daniel Zoran · Mike Chrzanowski · Daan Wierstra · Danilo Jimenez Rezende -
2019 Poster: Shaping Belief States with Generative Environment Models for RL »
Karol Gregor · Danilo Jimenez Rezende · Frederic Besse · Yan Wu · Hamza Merzic · Aaron van den Oord -
2017 Workshop: Machine Learning for Creativity and Design »
Douglas Eck · David Ha · S. M. Ali Eslami · Sander Dieleman · Rebecca Fiebrink · Luba Elliott -
2017 Poster: Imagination-Augmented Agents for Deep Reinforcement Learning »
Sébastien Racanière · Theophane Weber · David Reichert · Lars Buesing · Arthur Guez · Danilo Jimenez Rezende · Adrià Puigdomènech Badia · Oriol Vinyals · Nicolas Heess · Yujia Li · Razvan Pascanu · Peter Battaglia · Demis Hassabis · David Silver · Daan Wierstra -
2017 Oral: Imagination-Augmented Agents for Deep Reinforcement Learning »
Sébastien Racanière · Theophane Weber · David Reichert · Lars Buesing · Arthur Guez · Danilo Jimenez Rezende · Adrià Puigdomènech Badia · Oriol Vinyals · Nicolas Heess · Yujia Li · Razvan Pascanu · Peter Battaglia · Demis Hassabis · David Silver · Daan Wierstra -
2017 Poster: Variational Memory Addressing in Generative Models »
Jörg Bornschein · Andriy Mnih · Daniel Zoran · Danilo Jimenez Rezende -
2016 Poster: Unsupervised Learning of 3D Structure from Images »
Danilo Jimenez Rezende · S. M. Ali Eslami · Shakir Mohamed · Peter Battaglia · Max Jaderberg · Nicolas Heess -
2016 Poster: Attend, Infer, Repeat: Fast Scene Understanding with Generative Models »
S. M. Ali Eslami · Nicolas Heess · Theophane Weber · Yuval Tassa · David Szepesvari · koray kavukcuoglu · Geoffrey E Hinton -
2016 Poster: Towards Conceptual Compression »
Karol Gregor · Frederic Besse · Danilo Jimenez Rezende · Ivo Danihelka · Daan Wierstra -
2016 Poster: Interaction Networks for Learning about Objects, Relations and Physics »
Peter Battaglia · Razvan Pascanu · Matthew Lai · Danilo Jimenez Rezende · koray kavukcuoglu -
2015 Workshop: Black box learning and inference »
Josh Tenenbaum · Jan-Willem van de Meent · Tejas Kulkarni · S. M. Ali Eslami · Brooks Paige · Frank Wood · Zoubin Ghahramani -
2015 Poster: Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning »
Shakir Mohamed · Danilo Jimenez Rezende -
2014 Poster: Semi-supervised Learning with Deep Generative Models »
Diederik Kingma · Shakir Mohamed · Danilo Jimenez Rezende · Max Welling -
2014 Spotlight: Semi-supervised Learning with Deep Generative Models »
Diederik Kingma · Shakir Mohamed · Danilo Jimenez Rezende · Max Welling