6,343 research outputs found
Weakly supervised segmentation from extreme points
Annotation of medical images has been a major bottleneck for the development
of accurate and robust machine learning models. Annotation is costly and
time-consuming and typically requires expert knowledge, especially in the
medical domain. Here, we propose to use minimal user interaction in the form of
extreme point clicks in order to train a segmentation model that can, in turn,
be used to speed up the annotation of medical images. We use extreme points in
each dimension of a 3D medical image to constrain an initial segmentation based
on the random walker algorithm. This segmentation is then used as a weak
supervisory signal to train a fully convolutional network that can segment the
organ of interest based on the provided user clicks. We show that the network's
predictions can be refined through several iterations of training and
prediction using the same weakly annotated data. Ultimately, our method has the
potential to speed up the generation process of new training datasets for the
development of new machine learning and deep learning-based models for, but not
exclusively, medical image analysis.Comment: Accepted at the MICCAI Workshop for Large-scale Annotation of
Biomedical data and Expert Label Synthesis, Shenzen, China, 201
Weakly-Supervised Semantic Segmentation of Ships Using Thermal Imagery
The United States coastline spans 95,471 miles; a distance that cannot be
effectively patrolled or secured by manual human effort alone. Unmanned Aerial
Vehicles (UAVs) equipped with infrared cameras and deep-learning based
algorithms represent a more efficient alternative for identifying and
segmenting objects of interest - namely, ships. However, standard approaches to
training these algorithms require large-scale datasets of densely labeled
infrared maritime images. Such datasets are not publicly available and manually
annotating every pixel in a large-scale dataset would have an extreme labor
cost. In this work we demonstrate that, in the context of segmenting ships in
infrared imagery, weakly-supervising an algorithm with sparsely labeled data
can drastically reduce data labeling costs with minimal impact on system
performance. We apply weakly-supervised learning to an unlabeled dataset of
7055 infrared images sourced from the Naval Air Warfare Center Aircraft
Division (NAWCAD). We find that by sparsely labeling only 32 points per image,
weakly-supervised segmentation models can still effectively detect and segment
ships, with a Jaccard score of up to 0.756
Budget-aware Semi-Supervised Semantic and Instance Segmentation
Methods that move towards less supervised scenarios are key for image
segmentation, as dense labels demand significant human intervention. Generally,
the annotation burden is mitigated by labeling datasets with weaker forms of
supervision, e.g. image-level labels or bounding boxes. Another option are
semi-supervised settings, that commonly leverage a few strong annotations and a
huge number of unlabeled/weakly-labeled data. In this paper, we revisit
semi-supervised segmentation schemes and narrow down significantly the
annotation budget (in terms of total labeling time of the training set)
compared to previous approaches. With a very simple pipeline, we demonstrate
that at low annotation budgets, semi-supervised methods outperform by a wide
margin weakly-supervised ones for both semantic and instance segmentation. Our
approach also outperforms previous semi-supervised works at a much reduced
labeling cost. We present results for the Pascal VOC benchmark and unify weakly
and semi-supervised approaches by considering the total annotation budget, thus
allowing a fairer comparison between methods.Comment: To appear in CVPR-W 2019 (DeepVision workshop
Deep Extreme Cut: From Extreme Points to Object Segmentation
This paper explores the use of extreme points in an object (left-most,
right-most, top, bottom pixels) as input to obtain precise object segmentation
for images and videos. We do so by adding an extra channel to the image in the
input of a convolutional neural network (CNN), which contains a Gaussian
centered in each of the extreme points. The CNN learns to transform this
information into a segmentation of an object that matches those extreme points.
We demonstrate the usefulness of this approach for guided segmentation
(grabcut-style), interactive segmentation, video object segmentation, and dense
segmentation annotation. We show that we obtain the most precise results to
date, also with less user input, in an extensive and varied selection of
benchmarks and datasets. All our models and code are publicly available on
http://www.vision.ee.ethz.ch/~cvlsegmentation/dextr/.Comment: CVPR 2018 camera ready. Project webpage and code:
http://www.vision.ee.ethz.ch/~cvlsegmentation/dextr
Weakly supervised segment annotation via expectation kernel density estimation
Since the labelling for the positive images/videos is ambiguous in weakly
supervised segment annotation, negative mining based methods that only use the
intra-class information emerge. In these methods, negative instances are
utilized to penalize unknown instances to rank their likelihood of being an
object, which can be considered as a voting in terms of similarity. However,
these methods 1) ignore the information contained in positive bags, 2) only
rank the likelihood but cannot generate an explicit decision function. In this
paper, we propose a voting scheme involving not only the definite negative
instances but also the ambiguous positive instances to make use of the extra
useful information in the weakly labelled positive bags. In the scheme, each
instance votes for its label with a magnitude arising from the similarity, and
the ambiguous positive instances are assigned soft labels that are iteratively
updated during the voting. It overcomes the limitations of voting using only
the negative bags. We also propose an expectation kernel density estimation
(eKDE) algorithm to gain further insight into the voting mechanism.
Experimental results demonstrate the superiority of our scheme beyond the
baselines.Comment: 9 pages, 2 figure
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