Multi-modal brain images from MRI scans are widely used in clinical diagnosis to provide complementary information from different modalities. However, obtaining fully paired multi-modal images in practice is challenging due to various factors, such as time, cost, and artifacts, resulting in modality-missing brain images. To address this problem, unsupervised multi-modal brain image translation has been extensively studied. Existing methods suffer from the problem of brain tumor deformation during translation, as they fail to focus on the tumor areas when translating the whole images. In this paper, we propose an unsupervised tumor-aware distillation teacher-student network called UTAD-Net, which is capable of perceiving and translating tumor areas precisely. Specifically, our model consists of two parts: a teacher network and a student network. The teacher network learns an end-to-end mapping from source to target modality using unpaired images and corresponding tumor masks first. Then, the translation knowledge is distilled into the student network, enabling it to generate more realistic tumor areas and whole images without masks. Experiments show that our model achieves competitive performance on both quantitative and qualitative evaluations of image quality compared with state-of-the-art methods. Furthermore, we demonstrate the effectiveness of the generated images on downstream segmentation tasks. Our code is available at https://github.com/scut-HC/UTAD-Net.

In clinical practice, well-aligned multi-modal images, such as Magnetic Resonance (MR) and Computed Tomography (CT), together can provide complementary information for image-guided therapies. Multi-modal image registration is essential for the accurate alignment of these multi-modal images. However, it remains a very challenging task due to complicated and unknown spatial correspondence between different modalities. In this paper, we propose a novel translation-based unsupervised deformable image registration approach to convert the multi-modal registration problem to a mono-modal one. Specifically, our approach incorporates a discriminator-free translation network to facilitate the training of the registration network and a patchwise contrastive loss to encourage the translation network to preserve object shapes. Furthermore, we propose to replace an adversarial loss, that is widely used in previous multi-modal image registration methods, with a pixel loss in order to integrate the output of translation into the target modality. This leads to an unsupervised method requiring no ground-truth deformation or pairs of aligned images for training. We evaluate four variants of our approach on the public Learn2Reg 2021 datasets. The experimental results demonstrate that the proposed architecture achieves state-of-the-art performance. Our code is available at https://github.com/heyblackC/DFMIR.

We introduce the idea of inter-slice image augmentation whereby the numbers of the medical images and the corresponding segmentation labels are increased between two consecutive images in order to boost medical image segmentation accuracy. Unlike conventional data augmentation methods in medical imaging, which only increase the number of training samples directly by adding new virtual samples using simple parameterized transformations such as rotation, flipping, scaling, etc., we aim to augment data based on the relationship between two consecutive images, which increases not only the number but also the information of training samples. For this purpose, we propose a frame-interpolation-based data augmentation method to generate intermediate medical images and the corresponding segmentation labels between two consecutive images. We train and test a supervised U-Net liver segmentation network on SLIVER07 and CHAOS2019, respectively, with the augmented training samples, and obtain segmentation scores exhibiting significant improvement compared to the conventional augmentation methods.

Time series with missing values (incomplete time series) are ubiquitous in real life on account of noise or malfunctioning sensors. Time-series imputation (replacing missing data) remains a challenge due to the potential for nonlinear dependence on concurrent and previous values of the time series. In this paper, we propose a novel framework for modeling incomplete time series, called a linear memory vector recurrent neural network (LIME-RNN), a recurrent neural network (RNN) with a learned linear combination of previous history states. The technique bears some similarity to residual networks and graph-based temporal dependency imputation. In particular, we introduce a linear memory vector [called the residual sum vector (RSV)] that integrates over previous hidden states of the RNN, and is used to fill in missing values. A new loss function is developed to train our model with time series in the presence of missing values in an end-to-end way. Our framework can handle imputation of both missing-at-random and consecutive missing inputs. Moreover, when conducting time-series prediction with missing values, LIME-RNN allows imputation and prediction simultaneously. We demonstrate the efficacy of the model via extensive experimental evaluation on univariate and multivariate time series, achieving state-of-the-art performance on synthetic and real-world data. The statistical results show that our model is significantly better than most existing time-series univariate or multivariate imputation methods.

Previous studies have shown that attention mechanisms and shortest dependency paths have a positive effect on relation classification. In this paper, a keyword-attentive sentence mechanism is proposed to effectively combine the two methods. Furthermore, to effectively handle the imbalanced classification problem, this paper proposes a new loss function called the synthetic stimulation loss, which uses a modulating factor to allow the model to focus on hard-to-classify samples. The proposed two methods are integrated into a bidirectional gated recurrent unit (BiGRU). As a single model is not strong in noise immunity, this paper applies the mutual learning method to our model and forces the networks to teach each other. Therefore, we call the final model SSL-KAS-MuBiGRU. Experiments on the SemEval-2010 Task 8 data set and the TAC40 data set demonstrate that the keyword-attentive sentence mechanism and synthetic stimulation loss are useful for relation classification, and our model achieves state-of-the-art results.

Precise cancer classification is essential to the successful diagnosis and treatment of cancers. Although semisupervised dimensionality reduction approaches perform very well on clean datasets, the topology of the neighborhood constructed with most existing approaches is unstable in the presence of high-dimensional data with noise. In order to solve this problem, a novel local and global preserving semisupervised dimensionality reduction based on random subspace algorithm marked as RSLGSSDR, which utilizes random subspace for semisupervised dimensionality reduction, is proposed. The algorithm first designs multiple diverse graphs on different random subspace of datasets and then fuses these graphs into a mixture graph on which dimensionality reduction is performed. As themixture graph is constructed in lower dimensionality, it can ease the issues on graph construction on highdimensional samples such that it can hold complicated geometric distribution of datasets as the diversity of random subspaces. Experimental results on public gene expression datasets demonstrate that the proposed RSLGSSDR not only has superior recognition performance to competitive methods, but also is robust against a wide range of values of input parameters.

A new learning algorithm for categorical data, named CRN (Classification by Rule-based Neighbors) is proposed in this paper. CRN is a nonmetric and parameter-free classifier, and can be regarded as a hybrid of rule induction and instance-based learning. Based on a new measure of attributes quality and the separate-and-conquer strategy, CRN learns a collection of feature sets such that for each pair of instances belonging to different classes, there is a feature set on which the two instances disagree. For an unlabeled instance I and a labeled instance I', I' is a neighbor of I if and only if they agree on all attributes of a feature set. Then, CRN classifies an unlabeled instance I based on I's neighbors on those learned feature sets. To validate the performance of CRN, CRN is compared with six state-of-the-art classifiers on twenty-four datasets. Experimental results demonstrate that although the underlying idea of CRN is simple, the predictive accuracy of CRN is comparable to or better than that of the state-of-the-art classifiers on most datasets.