A cross-center smoothness prior for variational Bayesian brain tissue segmentation

Suppose one is faced with the challenge of tissue segmentation in MR images, without annotators at their center to provide labeled training data. One option is to go to another medical center for a trained classifier. Sadly, tissue classifiers do not generalize well across centers due to voxel intensity shifts caused by center-specific acquisition protocols. However, certain aspects of segmentations, such as spatial smoothness, remain relatively consistent and can be learned separately. Here we present a smoothness prior that is fit to segmentations produced at another medical center. This informative prior is presented to an unsupervised Bayesian model. The model clusters the voxel intensities, such that it produces segmentations that are similarly smooth to those of the other medical center. In addition, the unsupervised Bayesian model is extended to a semi-supervised variant, which needs no visual interpretation of clusters into tissues.Comment: 12 pages, 2 figures, 1 table. Accepted to the International Conference on Information Processing in Medical Imaging (2019

A Projected Subgradient Method for Scalable Multi-Task Learning

Recent approaches to multi-task learning have investigated the use of a variety of matrix norm regularization schemes for promoting feature sharing across tasks.In essence, these approaches aim at extending the l1 framework for sparse single task approximation to the multi-task setting. In this paper we focus on the computational complexity of training a jointly regularized model and propose an optimization algorithm whose complexity is linear with the number of training examples and O(n log n) with n being the number of parameters of the joint model. Our algorithm is based on setting jointly regularized loss minimization as a convex constrained optimization problem for which we develop an efficient projected gradient algorithm. The main contribution of this paper is the derivation of a gradient projection method with l1ââ constraints that can be performed efficiently and which has convergence rates

Transferring Nonlinear Representations using Gaussian Processes with a Shared Latent Space

When a series of problems are related, representations derived from learning earlier tasks may be useful in solving later problems. In this paper we propose a novel approach to transfer learning with low-dimensional, non-linear latent spaces. We show how such representations can be jointly learned across multiple tasks in a Gaussian Process framework. When transferred to new tasks with relatively few training examples, learning can be faster and/or more accurate. Experiments on digit recognition and newsgroup classification tasks show significantly improved performance when compared to baseline performance with a representation derived from a semi-supervised learning approach or with a discriminative approach that uses only the target data

Score Function Features for Discriminative Learning: Matrix and Tensor Framework

Feature learning forms the cornerstone for tackling challenging learning problems in domains such as speech, computer vision and natural language processing. In this paper, we consider a novel class of matrix and tensor-valued features, which can be pre-trained using unlabeled samples. We present efficient algorithms for extracting discriminative information, given these pre-trained features and labeled samples for any related task. Our class of features are based on higher-order score functions, which capture local variations in the probability density function of the input. We establish a theoretical framework to characterize the nature of discriminative information that can be extracted from score-function features, when used in conjunction with labeled samples. We employ efficient spectral decomposition algorithms (on matrices and tensors) for extracting discriminative components. The advantage of employing tensor-valued features is that we can extract richer discriminative information in the form of an overcomplete representations. Thus, we present a novel framework for employing generative models of the input for discriminative learning.Comment: 29 page

Joint Intermodal and Intramodal Label Transfers for Extremely Rare or Unseen Classes

In this paper, we present a label transfer model from texts to images for image classification tasks. The problem of image classification is often much more challenging than text classification. On one hand, labeled text data is more widely available than the labeled images for classification tasks. On the other hand, text data tends to have natural semantic interpretability, and they are often more directly related to class labels. On the contrary, the image features are not directly related to concepts inherent in class labels. One of our goals in this paper is to develop a model for revealing the functional relationships between text and image features as to directly transfer intermodal and intramodal labels to annotate the images. This is implemented by learning a transfer function as a bridge to propagate the labels between two multimodal spaces. However, the intermodal label transfers could be undermined by blindly transferring the labels of noisy texts to annotate images. To mitigate this problem, we present an intramodal label transfer process, which complements the intermodal label transfer by transferring the image labels instead when relevant text is absent from the source corpus. In addition, we generalize the inter-modal label transfer to zero-shot learning scenario where there are only text examples available to label unseen classes of images without any positive image examples. We evaluate our algorithm on an image classification task and show the effectiveness with respect to the other compared algorithms.Comment: The paper has been accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence. It will apear in a future issu

Limits on transfer learning from photographic image data to X-ray threat detection

BACKGROUND: X-ray imaging is a crucial and ubiquitous tool for detecting threats to transport security, but interpretation of the images presents a logistical bottleneck. Recent advances in Deep Learning image classification offer hope of improving throughput through automation. However, Deep Learning methods require large quantities of labelled training data. While photographic data is cheap and plentiful, comparable training sets are seldom available for the X-ray domain. OBJECTIVE: To determine whether and to what extent it is feasible to exploit the availability of photo data to supplement the training of X-ray threat detectors. METHODS: A new dataset was collected, consisting of 1901 matched pairs of photo & X-ray images of 501 common objects. Of these, 258 pairs were of 69 objects considered threats in the context of aviation. This data was used to test a variety of transfer learning approaches. A simple model of threat cue availability was developed to understand the limits of this transferability. RESULTS: Appearance features learned from photos provide a useful basis for training classifiers. Some transfer from the photo to the X-ray domain is possible as ∼40% of danger cues are shared between the modalities, but the effectiveness of this transfer is limited since ∼60% of cues are not. CONCLUSIONS: Transfer learning is beneficial when X-ray data is very scarce—of the order of tens of training images in our experiments—but provides no significant benefit when hundreds or thousands of X-ray images are available