Single-Image-Based Deep Learning for Segmentation of Early Esophageal Cancer Lesions

Accurate segmentation of lesions is crucial for diagnosis and treatment of early esophageal cancer (EEC). However, neither traditional nor deep learning-based methods up to today can meet the clinical requirements, with the mean Dice score - the most important metric in medical image analysis - hardly exceeding 0.75. In this paper, we present a novel deep learning approach for segmenting EEC lesions. Our approach stands out for its uniqueness, as it relies solely on a single image coming from one patient, forming the so-called "You-Only-Have-One" (YOHO) framework. On one hand, this "one-image-one-network" learning ensures complete patient privacy as it does not use any images from other patients as the training data. On the other hand, it avoids nearly all generalization-related problems since each trained network is applied only to the input image itself. In particular, we can push the training to "over-fitting" as much as possible to increase the segmentation accuracy. Our technical details include an interaction with clinical physicians to utilize their expertise, a geometry-based rendering of a single lesion image to generate the training set (the \emph{biggest} novelty), and an edge-enhanced UNet. We have evaluated YOHO over an EEC data-set created by ourselves and achieved a mean Dice score of 0.888, which represents a significant advance toward clinical applications

Time-reversal symmetry breaking driven topological phase transition in EuB6_6

The interplay between time-reversal symmetry (TRS) and band topology plays a crucial role in topological states of quantum matter. In time-reversal-invariant (TRI) systems, the inversion of spin-degenerate bands with opposite parity leads to nontrivial topological states, such as topological insulators and Dirac semimetals. When the TRS is broken, the exchange field induces spin splitting of the bands. The inversion of a pair of spin-splitting subbands can generate more exotic topological states, such as quantum anomalous Hall insulators and magnetic Weyl semimetals. So far, such topological phase transitions driven by the TRS breaking have not been visualized. In this work, using angle-resolved photoemission spectroscopy, we have demonstrated that the TRS breaking induces a band inversion of a pair of spin-splitting subbands at the TRI points of Brillouin zone in EuB$_6$, when a long-range ferromagnetic order is developed. The dramatic changes in the electronic structure result in a topological phase transition from a TRI ordinary insulator state to a TRS-broken topological semimetal (TSM) state. Remarkably, the magnetic TSM state has an ideal electronic structure, in which the band crossings are located at the Fermi level without any interference from other bands. Our findings not only reveal the topological phase transition driven by the TRS breaking, but also provide an excellent platform to explore novel physical behavior in the magnetic topological states of quantum matter.Comment: 22 pages, 7 figures, accepted by Phys. Rev.

A novel approach for lie detection based on F-score and extreme learning machine.

A new machine learning method referred to as F-score_ELM was proposed to classify the lying and truth-telling using the electroencephalogram (EEG) signals from 28 guilty and innocent subjects. Thirty-one features were extracted from the probe responses from these subjects. Then, a recently-developed classifier called extreme learning machine (ELM) was combined with F-score, a simple but effective feature selection method, to jointly optimize the number of the hidden nodes of ELM and the feature subset by a grid-searching training procedure. The method was compared to two classification models combining principal component analysis with back-propagation network and support vector machine classifiers. We thoroughly assessed the performance of these classification models including the training and testing time, sensitivity and specificity from the training and testing sets, as well as network size. The experimental results showed that the number of the hidden nodes can be effectively optimized by the proposed method. Also, F-score_ELM obtained the best classification accuracy and required the shortest training and testing time

Analysis on differential gene expression data for prediction of new biological features in permanent atrial fibrillation

Permanent Atrial fibrillation (pmAF) has largely remained incurable since the existing information for explaining precise mechanisms underlying pmAF is not sufficient. Microarray analysis offers a broader and unbiased approach to identify and predict new biological features of pmAF. By considering the unbalanced sample numbers in most microarray data of case - control, we designed an asymmetric principal component analysis algorithm and applied it to re - analyze differential gene expression data of pmAF patients and control samples for predicting new biological features. Finally, we identified 51 differentially expressed genes using the proposed method, in which 42 differentially expressed genes are new findings compared with two related works on the same data and the existing studies. The enrichment analysis illustrated the reliability of identified differentially expressed genes. Moreover, we predicted three new pmAF – related signaling pathways using the identified differentially expressed genes via the KO-Based Annotation System. Our analysis and the existing studies supported that the predicted signaling pathways may promote the pmAF progression. The results above are worthy to do further experimental studies. This work provides some new insights into molecular features of pmAF. It has also the potentially important implications for improved understanding of the molecular mechanisms of pmAF.Published versio

Analysis of potential roles of combinatorial microRNA regulation in occurrence of valvular heart disease with atrial fibrillation based on computational evidences.

BACKGROUND:Atrial fibrillation (AF) is the most common arrhythmia. Patients with valvular heart disease (VHD) frequently have AF. Growing evidence demonstrates that a specifically altered pattern of microRNA (miRNA) expression is related to valvular heart disease with atrial fibrillation (AF-VHD) processes. However, the combinatorial regulation by multiple miRNAs in inducing AF-VHD remains largely unknown. METHODS:The work identified AF-VHD-specific miRNAs and their combinations through mapping miRNA expression profile into differential co-expression network. The expressions of some dysregulated miRNAs were measured by quantitative reverse transcription polymerase chain reaction (qRT-PCR). The regulations of signaling pathways by the combinatorial miRNAs were predicted by enrichment analysis tools. RESULTS:Thirty-two differentially expressed (DE) miRNAs were identified to be AF-VHD-specific, some of which were new findings. These miRNAs interacted to form 5 combinations. qRT-PCR confirmed the different expression of several identified miRNAs, which illustrated the reliability and biomarker potentials of 32 dysregulation miRNAs. The biological characteristics of combinatorial miRNAs related to AF-VHD were highlighted. Twelve signaling pathways regulated by combinatorial miRNAs were predicted to be possibly associated with AF-VHD. CONCLUSIONS:The AF-VHD-related signaling pathways regulated by combinatorial miRNAs may play an important role in the occurrence of AF-VHD. The work brings new insights into biomarkers and miRNA combination regulation mechanism in AF-VHD as well as further biological experiments