4 research outputs found
Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning
We address the problem of segmenting 3D multi-modal medical images in
scenarios where very few labeled examples are available for training.
Leveraging the recent success of adversarial learning for semi-supervised
segmentation, we propose a novel method based on Generative Adversarial
Networks (GANs) to train a segmentation model with both labeled and unlabeled
images. The proposed method prevents over-fitting by learning to discriminate
between true and fake patches obtained by a generator network. Our work extends
current adversarial learning approaches, which focus on 2D single-modality
images, to the more challenging context of 3D volumes of multiple modalities.
The proposed method is evaluated on the problem of segmenting brain MRI from
the iSEG-2017 and MRBrainS 2013 datasets. Significant performance improvement
is reported, compared to state-of-art segmentation networks trained in a
fully-supervised manner. In addition, our work presents a comprehensive
analysis of different GAN architectures for semi-supervised segmentation,
showing recent techniques like feature matching to yield a higher performance
than conventional adversarial training approaches. Our code is publicly
available at https://github.com/arnab39/FewShot_GAN-Unet3DComment: submitted to Medical Image Analysis for revie
OMNIA Faster R-CNN: Detection in the wild through dataset merging and soft distillation
Object detectors tend to perform poorly in new or open domains, and require
exhaustive yet costly annotations from fully labeled datasets. We aim at
benefiting from several datasets with different categories but without
additional labelling, not only to increase the number of categories detected,
but also to take advantage from transfer learning and to enhance domain
independence.
Our dataset merging procedure starts with training several initial Faster
R-CNN on the different datasets while considering the complementary datasets'
images for domain adaptation. Similarly to self-training methods, the
predictions of these initial detectors mitigate the missing annotations on the
complementary datasets. The final OMNIA Faster R-CNN is trained with all
categories on the union of the datasets enriched by predictions. The joint
training handles unsafe targets with a new classification loss called SoftSig
in a softly supervised way.
Experimental results show that in the case of fashion detection for images in
the wild, merging Modanet with COCO increases the final performance from 45.5%
to 57.4% in mAP. Applying our soft distillation to the task of detection with
domain shift between GTA and Cityscapes enables to beat the state-of-the-art by
5.3 points. Our methodology could unlock object detection for real-world
applications without immense datasets.Comment: 9 pages, 5 figures, 4 table
Revisiting CycleGAN for semi-supervised segmentation
In this work, we study the problem of training deep networks for semantic
image segmentation using only a fraction of annotated images, which may
significantly reduce human annotation efforts. Particularly, we propose a
strategy that exploits the unpaired image style transfer capabilities of
CycleGAN in semi-supervised segmentation. Unlike recent works using adversarial
learning for semi-supervised segmentation, we enforce cycle consistency to
learn a bidirectional mapping between unpaired images and segmentation masks.
This adds an unsupervised regularization effect that boosts the segmentation
performance when annotated data is limited. Experiments on three different
public segmentation benchmarks (PASCAL VOC 2012, Cityscapes and ACDC)
demonstrate the effectiveness of the proposed method. The proposed model
achieves 2-4% of improvement with respect to the baseline and outperforms
recent approaches for this task, particularly in low labeled data regime
Boosting Semi-supervised Image Segmentation with Global and Local Mutual Information Regularization
The scarcity of labeled data often impedes the application of deep learning
to the segmentation of medical images. Semi-supervised learning seeks to
overcome this limitation by exploiting unlabeled examples in the learning
process. In this paper, we present a novel semi-supervised segmentation method
that leverages mutual information (MI) on categorical distributions to achieve
both global representation invariance and local smoothness. In this method, we
maximize the MI for intermediate feature embeddings that are taken from both
the encoder and decoder of a segmentation network. We first propose a global MI
loss constraining the encoder to learn an image representation that is
invariant to geometric transformations. Instead of resorting to
computationally-expensive techniques for estimating the MI on continuous
feature embeddings, we use projection heads to map them to a discrete cluster
assignment where MI can be computed efficiently. Our method also includes a
local MI loss to promote spatial consistency in the feature maps of the decoder
and provide a smoother segmentation. Since mutual information does not require
a strict ordering of clusters in two different assignments, we incorporate a
final consistency regularization loss on the output which helps align the
cluster labels throughout the network. We evaluate the method on four
challenging publicly-available datasets for medical image segmentation.
Experimental results show our method to outperform recently-proposed approaches
for semi-supervised segmentation and provide an accuracy near to full
supervision while training with very few annotated images