Multi-organ segmentation is a critical problem in medical image analysis due to its great value for computer-aided diagnosis, computer-aided surgery, and radiation therapy. Although fully-supervised segmentation methods can achieve good performance, they usually require a large amount of 3D data, such as CT scans, with voxel-wised annotations which are usually difficult, expensive, and slow to obtain. By contrast, large unannotated datasets of CT images are available. Inspired by the well-known semi-supervised learning framework co-training, we propose multi-planar co-training (MPCT), to generate more reliable pseudo-labels by enforcing consistency among multiple planes, i.e., saggital, coronal, and axial planes, of 3D unlabeled medical data, which play a vital role in our framework. Empirical results show that generating pseudo-labels by the multi-planar fusion rather than a single plane leads to a significant performance gain. We evaluate our approach on a new collected dataset and show that MPCT boosts the performance of a typical segmentation model, fully convolutional networks, by a large margin, when only a small set of labeled 3D data is available, i.e., 77.49 vs. 73.14 .
Co-training @cite_49 assumes that (1) features can be split into two independent sets and (2) each sub-feature set is sufficient to train a good classifier. During the learning process, each classifier is retrained with the additional training examples given by the other classifier. Co-training utilizes multiple sets of independent features which describe the same data, and therefore tends to yield more accurate and robust results than self-training @cite_11 . Multi-view learning @cite_0 , in general, defines learning paradigms that utilize the agreement among different learners. Co-training is one of the earliest schemes for multi-view learning.
@cite_0 @cite_49 @cite_11
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In recent years, a great many methods of learning from multi-view data by considering the diversity of different views have been proposed. These views may be obtained from multiple sources or different feature subsets. In trying to organize and highlight similarities and differences between the variety of multi-view learning approaches, we review a number of representative multi-view learning algorithms in different areas and classify them into three groups: 1) co-training, 2) multiple kernel learning, and 3) subspace learning. Notably, co-training style algorithms train alternately to maximize the mutual agreement on two distinct views of the data; multiple kernel learning algorithms exploit kernels that naturally correspond to different views and combine kernels either linearly or non-linearly to improve learning performance; and subspace learning algorithms aim to obtain a latent subspace shared by multiple views by assuming that the input views are generated from this latent subspace. Though there is significant variance in the approaches to integrating multiple views to improve learning performance, they mainly exploit either the consensus principle or the complementary principle to ensure the success of multi-view learning. Since accessing multiple views is the fundament of multi-view learning, with the exception of study on learning a model from multiple views, it is also valuable to study how to construct multiple views and how to evaluate these views. Overall, by exploring the consistency and complementary properties of different views, multi-view learning is rendered more effective, more promising, and has better generalization ability than single-view learning. Multiple kernel learning approaches to multi-view learning [1, 11, 7] have recently become very popular since they can easily combine information from multiple views, e.g., by adding or multiplying kernels. They are particularly effective when the views are class conditionally independent, since the errors committed by each view can be corrected by the other views. Most methods assume that a single set of kernel weights is sufficient for accurate classification, however, one can expect that the set of features important to discriminate between different examples can vary locally. As a result the performance of such global techniques can degrade in the presence of complex noise processes, e.g., heteroscedastic noise, missing data, or when the discriminative properties vary across the input space. The construction of robust multimodal interfaces often requires large amounts of labeled training data to account for cross-user differences and variation in the environment. In this work, we investigate whether unlabeled training data can be leveraged to build more reliable audio-visual classifiers through co-training, a multi-view learning algorithm. Multimodal tasks are good candidates for multi-view learning, since each modality provides a potentially redundant view to the learning algorithm. We apply co-training to two problems: audio-visual speech unit classification, and user agreement recognition using spoken utterances and head gestures. We demonstrate that multimodal co-training can be used to learn from only a few labeled examples in one or both of the audio-visual modalities. We also propose a co-adaptation algorithm, which adapts existing audio-visual classifiers to a particular user or noise condition by leveraging the redundancy in the unlabeled data. Due to the globalization on the Web, many companies and institutions need to efficiently organize and search repositories containing multilingual documents. The management of these heterogeneous text collections increases the costs significantly because experts of different languages are required to organize these collections. Cross-language text categorization can provide techniques to extend existing automatic classification systems in one language to new languages without requiring additional intervention of human experts. In this paper, we propose a learning algorithm based on the EM scheme which can be used to train text classifiers in a multilingual environment. In particular, in the proposed approach, we assume that a predefined category set and a collection of labeled training data is available for a given language L sub 1 . A classifier for a different language L sub 2 is trained by translating the available labeled training set for L sub 1 to L sub 2 and by using an additional set of unlabeled documents from L sub 2 . This technique allows us to extract correct statistical properties of the language L sub 2 which are not completely available in automatically translated examples, because of the different characteristics of language L sub 1 and of the approximation of the translation process. Our experimental results show that the performance of the proposed method is very promising when applied on a test document set extracted from newsgroups in English and Italian.