Probabilistic Integration of Object Level Annotations in Chest X-ray Classification

Medical image datasets and their annotations are not growing as fast as their equivalents in the general domain. This makes translation from the newest, more data-intensive methods that have made a large impact on the vision field increasingly more difficult and less efficient. In this paper, we propose a new probabilistic latent variable model for disease classification in chest X-ray images. Specifically we consider chest X-ray datasets that contain global disease labels, and for a smaller subset contain object level expert annotations in the form of eye gaze patterns and disease bounding boxes. We propose a two-stage optimization algorithm which is able to handle these different label granularities through a single training pipeline in a two-stage manner. In our pipeline global dataset features are learned in the lower level layers of the model. The specific details and nuances in the fine-grained expert object-level annotations are learned in the final layers of the model using a knowledge distillation method inspired by conditional variational inference. Subsequently, model weights are frozen to guide this learning process and prevent overfitting on the smaller richly annotated data subsets. The proposed method yields consistent classification improvement across different back-bones on the common benchmark datasets Chest X-ray14 and MIMIC-CXR. This shows how two-stage learning of labels from coarse to fine-grained, in particular with object level annotations, is an effective method for more optimal annotation usage.</p

Intelectin contributes to allergen-induced IL-25, IL-33, and TSLP expression and type 2 response in asthma and atopic dermatitis.

The epithelial and epidermal innate cytokines IL-25, IL-33, and thymic stromal lymphopoietin (TSLP) have pivotal roles in the initiation of allergic inflammation in asthma and atopic dermatitis (AD). However, the mechanism by which the expression of these innate cytokines is regulated remains unclear. Intelectin (ITLN) is expressed in airway epithelial cells and promotes allergic airway inflammation. We hypothesized that ITLN is required for allergen-induced IL-25, IL-33, and TSLP expression. In two asthma models, Itln knockdown reduced allergen-induced increases in Il-25, Il-33, and Tslp and development of type 2 response, eosinophilic inflammation, mucus overproduction, and airway hyperresponsiveness. Itln knockdown also inhibited house dust mite (HDM)-induced early upregulation of Il-25, Il-33, and Tslp in a model solely inducing airway sensitization. Using human airway epithelial cells, we demonstrated that HDM-induced increases in ITLN led to phosphorylation of epidermal growth factor receptor and extracellular-signal regulated kinase, which were required for induction of IL-25, IL-33, and TSLP expression. In two AD models, Itln knockdown suppressed expression of Il-33, Tslp, and Th2 cytokines and eosinophilic inflammation. In humans, ITLN1 expression was significantly increased in asthmatic airways and in lesional skin of AD. We conclude that ITLN contributes to allergen-induced Il-25, Il-33, and Tslp expression in asthma and AD

The separability of tripartite Gaussian state with amplification and amplitude damping

Tripartite three mode Gaussian state undergoes parametric amplification and amplitude damping as well as thermal noise is studied. In the case of a state totally symmetrically interacting with the environment, the time dependent correlation matrix of the state in evolution is given. The conditions for fully separability and fully entanglement of the final tripartite three mode Gaussian state are worked out.Comment: 9 pages, 3 figure

Fermion localization on asymmetric two-field thick branes

In this paper we investigate the localization of fermions on asymmetric thick branes generated by two scalars $\phi$ and $\chi$. In order to trap fermions on the asymmetric branes with kink-like warp factors, the couplings with the background scalars $\eta\bar{\Psi}F(\chi,\phi)\Psi$ are introduced, where $F(\chi,\phi)$ is a function of $\phi$ and $\chi$. We find that the coupling $\eta\bar{\Psi}\chi\phi\Psi$ do not support the localization of 4-dimensional fermions on the branes. While, for the case $\eta\bar{\Psi}\chi\Psi+\eta'\bar{\Psi}\phi\Psi$, which is the kink-fermion coupling corresponding to one-scalar-generated brane scenarios, the zero mode of left-handed fermions could be trapped on the branes under some conditions.Comment: v2: 11 pages, 4 figures, accepted by CQ

Probing the lightest new gauge boson BHB_H in the littlest Higgs model via the processes γγ→ffˉBH\gamma\gamma \to f\bar{f}B_H at the ILC

The neutral gauge boson $B_H$ with the mass of hundreds GeV, is the lightest particle predicted by the littlest Higgs(LH) model, and such particle should be the first signal of the LH model at the planed ILC if it exists indeed. In this paper, we study some processes of the $B_H$ production associated with the fermion pair at the ILC, i.e., $\gamma\gamma\to f\bar{f}B_{H}$. The studies show that the most promising processes to detect $B_H$ among $\gamma\gamma\to f\bar{f}B_{H}$ are $\gamma\gamma\to l'^+l'^-B_{H}(l'=e,\mu)$, and they can produce the sufficient signals in most parameter space preferred by the electroweak precision data at the ILC. On the other hand, the signal produced via the certain $B_H$ decay modes is typical and such signal can be easily identified from the SM background. Therefore, $B_H$, the lightest gauge boson in the LH model would be detectable at the photon collider realized at the ILC.Comment: 12 pages, 4 figure

LifeLonger: A Benchmark for Continual Disease Classification

Deep learning models have shown a great effectiveness in recognition of findings in medical images. However, they cannot handle the ever-changing clinical environment, bringing newly annotated medical data from different sources. To exploit the incoming streams of data, these models would benefit largely from sequentially learning from new samples, without forgetting the previously obtained knowledge. In this paper we introduce LifeLonger, a benchmark for continual disease classification on the MedMNIST collection, by applying existing state-of-the-art continual learning methods. In particular, we consider three continual learning scenarios, namely, task and class incremental learning and the newly defined cross-domain incremental learning. Task and class incremental learning of diseases address the issue of classifying new samples without re-training the models from scratch, while cross-domain incremental learning addresses the issue of dealing with datasets originating from different institutions while retaining the previously obtained knowledge. We perform a thorough analysis of the performance and examine how the well-known challenges of continual learning, such as the catastrophic forgetting exhibit themselves in this setting. The encouraging results demonstrate that continual learning has a major potential to advance disease classification and to produce a more robust and efficient learning framework for clinical settings. The code repository, data partitions and baseline results for the complete benchmark will be made publicly available

A Cellular Automata Model with Probability Infection and Spatial Dispersion

In this article, we have proposed an epidemic model by using probability cellular automata theory. The essential mathematical features are analyzed with the help of stability theory. We have given an alternative modelling approach for the spatiotemporal system which is more realistic and satisfactory from the practical point of view. A discrete and spatiotemporal approach are shown by using cellular automata theory. It is interesting to note that both size of the endemic equilibrium and density of the individual increase with the increasing of the neighborhood size and infection rate, but the infections decrease with the increasing of the recovery rate. The stability of the system around the positive interior equilibrium have been shown by using suitable Lyapunov function. Finally experimental data simulation for SARS disease in China and a brief discussion conclude the paper