14,432 research outputs found
Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation
Medical image segmentation is a fundamental and critical step in many
image-guided clinical approaches. Recent success of deep learning-based
segmentation methods usually relies on a large amount of labeled data, which is
particularly difficult and costly to obtain especially in the medical imaging
domain where only experts can provide reliable and accurate annotations.
Semi-supervised learning has emerged as an appealing strategy and been widely
applied to medical image segmentation tasks to train deep models with limited
annotations. In this paper, we present a comprehensive review of recently
proposed semi-supervised learning methods for medical image segmentation and
summarized both the technical novelties and empirical results. Furthermore, we
analyze and discuss the limitations and several unsolved problems of existing
approaches. We hope this review could inspire the research community to explore
solutions for this challenge and further promote the developments in medical
image segmentation field
Geometry meets semantics for semi-supervised monocular depth estimation
Depth estimation from a single image represents a very exciting challenge in
computer vision. While other image-based depth sensing techniques leverage on
the geometry between different viewpoints (e.g., stereo or structure from
motion), the lack of these cues within a single image renders ill-posed the
monocular depth estimation task. For inference, state-of-the-art
encoder-decoder architectures for monocular depth estimation rely on effective
feature representations learned at training time. For unsupervised training of
these models, geometry has been effectively exploited by suitable images
warping losses computed from views acquired by a stereo rig or a moving camera.
In this paper, we make a further step forward showing that learning semantic
information from images enables to improve effectively monocular depth
estimation as well. In particular, by leveraging on semantically labeled images
together with unsupervised signals gained by geometry through an image warping
loss, we propose a deep learning approach aimed at joint semantic segmentation
and depth estimation. Our overall learning framework is semi-supervised, as we
deploy groundtruth data only in the semantic domain. At training time, our
network learns a common feature representation for both tasks and a novel
cross-task loss function is proposed. The experimental findings show how,
jointly tackling depth prediction and semantic segmentation, allows to improve
depth estimation accuracy. In particular, on the KITTI dataset our network
outperforms state-of-the-art methods for monocular depth estimation.Comment: 16 pages, Accepted to ACCV 201
Anatomically-aware Uncertainty for Semi-supervised Image Segmentation
Semi-supervised learning relaxes the need of large pixel-wise labeled
datasets for image segmentation by leveraging unlabeled data. A prominent way
to exploit unlabeled data is to regularize model predictions. Since the
predictions of unlabeled data can be unreliable, uncertainty-aware schemes are
typically employed to gradually learn from meaningful and reliable predictions.
Uncertainty estimation methods, however, rely on multiple inferences from the
model predictions that must be computed for each training step, which is
computationally expensive. Moreover, these uncertainty maps capture pixel-wise
disparities and do not consider global information. This work proposes a novel
method to estimate segmentation uncertainty by leveraging global information
from the segmentation masks. More precisely, an anatomically-aware
representation is first learnt to model the available segmentation masks. The
learnt representation thereupon maps the prediction of a new segmentation into
an anatomically-plausible segmentation. The deviation from the plausible
segmentation aids in estimating the underlying pixel-level uncertainty in order
to further guide the segmentation network. The proposed method consequently
estimates the uncertainty using a single inference from our representation,
thereby reducing the total computation. We evaluate our method on two publicly
available segmentation datasets of left atria in cardiac MRIs and of multiple
organs in abdominal CTs. Our anatomically-aware method improves the
segmentation accuracy over the state-of-the-art semi-supervised methods in
terms of two commonly used evaluation metrics.Comment: Accepted at Medical Image Analysis. Code is available at:
$\href{https://github.com/adigasu/Anatomically-aware_Uncertainty_for_Semi-supervised_Segmentation}{Github}
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