no code implementations • 4 Feb 2022 • Jizong Peng, Ping Wang, Marco Pedersoli, Christian Desrosiers
Unsupervised pre-training has been proven as an effective approach to boost various downstream tasks given limited labeled data.
no code implementations • 16 Nov 2021 • Jizong Peng, Christian Desrosiers, Marco Pedersoli
This work considers semi-supervised segmentation as a dense prediction problem based on prototype vector correlation and proposes a simple way to represent each segmentation class with multiple prototypes.
1 code implementation • NeurIPS 2021 • Jizong Peng, Ping Wang, Chrisitian Desrosiers, Marco Pedersoli
Pre-training a recognition model with contrastive learning on a large dataset of unlabeled data has shown great potential to boost the performance of a downstream task, e. g., image classification.
no code implementations • 12 Jul 2021 • Ping Wang, Jizong Peng, Marco Pedersoli, Yuanfeng Zhou, Caiming Zhang, Christian Desrosiers
Despite their outstanding accuracy, semi-supervised segmentation methods based on deep neural networks can still yield predictions that are considered anatomically impossible by clinicians, for instance, containing holes or disconnected regions.
1 code implementation • 8 Mar 2021 • Jizong Peng, Marco Pedersoli, Christian Desrosiers
In this method, we maximize the MI for intermediate feature embeddings that are taken from both the encoder and decoder of a segmentation network.
1 code implementation • 31 Oct 2020 • Ping Wang, Jizong Peng, Marco Pedersoli, Yuanfeng Zhou, Caiming Zhang, Christian Desrosiers
Moreover, to encourage predictions from different networks to be both consistent and confident, we enhance this generalized JSD loss with an uncertainty regularizer based on entropy.
no code implementations • MIDL 2019 • Jizong Peng, Marco Pedersoli, Christian Desrosiers
The scarcity of labeled data often limits the application of deep learning to medical image segmentation.
no code implementations • 3 Oct 2019 • Jizong Peng, Christian Desrosiers, Marco Pedersoli
The second, named Invariant Information Clustering (IIC), maximizes the mutual information between the clustering of a sample and its geometrically transformed version.
no code implementations • 2 Oct 2019 • Xuan Li, Yuchen Lu, Peng Xu, Jizong Peng, Christian Desrosiers, Xue Liu
In this paper, we study the problem of image recognition with non-differentiable constraints.
no code implementations • 15 Aug 2019 • Jizong Peng, Hoel Kervadec, Jose Dolz, Ismail Ben Ayed, Marco Pedersoli, Christian Desrosiers
An efficient strategy for weakly-supervised segmentation is to impose constraints or regularization priors on target regions.
2 code implementations • 27 Mar 2019 • Jizong Peng, Guillermo Estrada, Marco Pedersoli, Christian Desrosiers
In this paper, we aim to improve the performance of semantic image segmentation in a semi-supervised setting in which training is effectuated with a reduced set of annotated images and additional non-annotated images.