150,449 research outputs found
COSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-training
Deep learning models have demonstrated remarkable success in multi-organ
segmentation but typically require large-scale datasets with all organs of
interest annotated. However, medical image datasets are often low in sample
size and only partially labeled, i.e., only a subset of organs are annotated.
Therefore, it is crucial to investigate how to learn a unified model on the
available partially labeled datasets to leverage their synergistic potential.
In this paper, we systematically investigate the partial-label segmentation
problem with theoretical and empirical analyses on the prior techniques. We
revisit the problem from a perspective of partial label supervision signals and
identify two signals derived from ground truth and one from pseudo labels. We
propose a novel two-stage framework termed COSST, which effectively and
efficiently integrates comprehensive supervision signals with self-training.
Concretely, we first train an initial unified model using two ground
truth-based signals and then iteratively incorporate the pseudo label signal to
the initial model using self-training. To mitigate performance degradation
caused by unreliable pseudo labels, we assess the reliability of pseudo labels
via outlier detection in latent space and exclude the most unreliable pseudo
labels from each self-training iteration. Extensive experiments are conducted
on one public and three private partial-label segmentation tasks over 12 CT
datasets. Experimental results show that our proposed COSST achieves
significant improvement over the baseline method, i.e., individual networks
trained on each partially labeled dataset. Compared to the state-of-the-art
partial-label segmentation methods, COSST demonstrates consistent superior
performance on various segmentation tasks and with different training data
sizes
Multi-label classification models for heterogeneous data: an ensemble-based approach.
In recent years, the multi-label classification gained attention of the scientific community given its ability to solve real-world problems where each instance of the dataset may be associated with several class labels simultaneously, such as multimedia categorization or medical problems.
The first objective of this dissertation is to perform a thorough review of the state-of-the-art ensembles of multi-label classifiers (EMLCs). Its aim is twofold: 1) study state-of-the-art ensembles of multi-label classifiers and categorize them proposing a novel taxonomy; and 2) perform an experimental study to give some tips and guidelines to select the method that perform the best according to the characteristics of a given problem.
Since most of the EMLCs are based on creating diverse members by randomly selecting instances, input features, or labels, our main objective is to propose novel ensemble methods while considering the characteristics of the data. In this thesis, we propose two evolutionary algorithms to build EMLCs. The first proposal encodes an entire EMLC in each individual, where each member is focused on a small subset of the labels. On the other hand, the second algorithm encodes separate members in each individual, then combining the individuals of the population to build the ensemble. Finally, both methods are demonstrated to be more consistent and perform significantly better than state-of-the-art methods in multi-label classification
Multi-utility Learning: Structured-output Learning with Multiple Annotation-specific Loss Functions
Structured-output learning is a challenging problem; particularly so because
of the difficulty in obtaining large datasets of fully labelled instances for
training. In this paper we try to overcome this difficulty by presenting a
multi-utility learning framework for structured prediction that can learn from
training instances with different forms of supervision. We propose a unified
technique for inferring the loss functions most suitable for quantifying the
consistency of solutions with the given weak annotation. We demonstrate the
effectiveness of our framework on the challenging semantic image segmentation
problem for which a wide variety of annotations can be used. For instance, the
popular training datasets for semantic segmentation are composed of images with
hard-to-generate full pixel labellings, as well as images with easy-to-obtain
weak annotations, such as bounding boxes around objects, or image-level labels
that specify which object categories are present in an image. Experimental
evaluation shows that the use of annotation-specific loss functions
dramatically improves segmentation accuracy compared to the baseline system
where only one type of weak annotation is used
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