Exploring Feature Representation Learning for Semi-supervised Medical Image Segmentation

This paper presents a simple yet effective two-stage framework for semi-supervised medical image segmentation. Our key insight is to explore the feature representation learning with labeled and unlabeled (i.e., pseudo labeled) images to enhance the segmentation performance. In the first stage, we present an aleatoric uncertainty-aware method, namely AUA, to improve the segmentation performance for generating high-quality pseudo labels. Considering the inherent ambiguity of medical images, AUA adaptively regularizes the consistency on images with low ambiguity. To enhance the representation learning, we propose a stage-adaptive contrastive learning method, including a boundary-aware contrastive loss to regularize the labeled images in the first stage and a prototype-aware contrastive loss to optimize both labeled and pseudo labeled images in the second stage. The boundary-aware contrastive loss only optimizes pixels around the segmentation boundaries to reduce the computational cost. The prototype-aware contrastive loss fully leverages both labeled images and pseudo labeled images by building a centroid for each class to reduce computational cost for pair-wise comparison. Our method achieves the best results on two public medical image segmentation benchmarks. Notably, our method outperforms the prior state-of-the-art by 5.7% on Dice for colon tumor segmentation relying on just 5% labeled images.Comment: On submission to TM

Semi-Supervised Deep Regression with Uncertainty Consistency and Variational Model Ensembling via Bayesian Neural Networks

Deep regression is an important problem with numerous applications. These range from computer vision tasks such as age estimation from photographs, to medical tasks such as ejection fraction estimation from echocardiograms for disease tracking. Semi-supervised approaches for deep regression are notably under-explored compared to classification and segmentation tasks, however. Unlike classification tasks, which rely on thresholding functions for generating class pseudo-labels, regression tasks use real number target predictions directly as pseudo-labels, making them more sensitive to prediction quality. In this work, we propose a novel approach to semi-supervised regression, namely Uncertainty-Consistent Variational Model Ensembling (UCVME), which improves training by generating high-quality pseudo-labels and uncertainty estimates for heteroscedastic regression. Given that aleatoric uncertainty is only dependent on input data by definition and should be equal for the same inputs, we present a novel uncertainty consistency loss for co-trained models. Our consistency loss significantly improves uncertainty estimates and allows higher quality pseudo-labels to be assigned greater importance under heteroscedastic regression. Furthermore, we introduce a novel variational model ensembling approach to reduce prediction noise and generate more robust pseudo-labels. We analytically show our method generates higher quality targets for unlabeled data and further improves training. Experiments show that our method outperforms state-of-the-art alternatives on different tasks and can be competitive with supervised methods that use full labels. Our code is available at https://github.com/xmed-lab/UCVME.Comment: Accepted by AAAI2

Performance of Automotive Industry under Financial Crisis

Since the financial crisis, automobile industry faces severe problems such as sale decline and cash shortage. Some corporations such as General Motors also went to bankrupt. The whole industry was under financial distress and this also affected other related industries such as part manufacturers and suppliers. Most literatures investigated the industry performance by measures such as sale and production volume, export record or GDP contribution. Few of them examine the industrial performance using financial ratio analysis or highlight the key financial indicators in the automobile industry. There are a number of financial ratios that are more important than the others for different industries. This research applies factor analysis to determine the key financial indicators extracted from some financial ratios to analyse the global automobile industry. Four financial indicators are crucial in auto industry and their importance are ranked as ‘Liquidity’, ‘Activity management’, ‘Cash flow’ and ‘Profitability’. It also applies the four indicators to General Motors bankruptcy case and examines reasons of a specific company failure. This study contributes a lot to previous literatures as an empirical example in areas such as factor analysis, performance measure, crisis theory and bankruptcy discuss

Morphology-inspired Unsupervised Gland Segmentation via Selective Semantic Grouping

Designing deep learning algorithms for gland segmentation is crucial for automatic cancer diagnosis and prognosis, yet the expensive annotation cost hinders the development and application of this technology. In this paper, we make a first attempt to explore a deep learning method for unsupervised gland segmentation, where no manual annotations are required. Existing unsupervised semantic segmentation methods encounter a huge challenge on gland images: They either over-segment a gland into many fractions or under-segment the gland regions by confusing many of them with the background. To overcome this challenge, our key insight is to introduce an empirical cue about gland morphology as extra knowledge to guide the segmentation process. To this end, we propose a novel Morphology-inspired method via Selective Semantic Grouping. We first leverage the empirical cue to selectively mine out proposals for gland sub-regions with variant appearances. Then, a Morphology-aware Semantic Grouping module is employed to summarize the overall information about the gland by explicitly grouping the semantics of its sub-region proposals. In this way, the final segmentation network could learn comprehensive knowledge about glands and produce well-delineated, complete predictions. We conduct experiments on GlaS dataset and CRAG dataset. Our method exceeds the second-best counterpart over 10.56% at mIOU.Comment: MICCAI 2023 Accepte

Radiomics-Informed Deep Learning for Classification of Atrial Fibrillation Sub-Types from Left-Atrium CT Volumes

Atrial Fibrillation (AF) is characterized by rapid, irregular heartbeats, and can lead to fatal complications such as heart failure. The disease is divided into two sub-types based on severity, which can be automatically classified through CT volumes for disease screening of severe cases. However, existing classification approaches rely on generic radiomic features that may not be optimal for the task, whilst deep learning methods tend to over-fit to the high-dimensional volume inputs. In this work, we propose a novel radiomics-informed deep-learning method, RIDL, that combines the advantages of deep learning and radiomic approaches to improve AF sub-type classification. Unlike existing hybrid techniques that mostly rely on na\"ive feature concatenation, we observe that radiomic feature selection methods can serve as an information prior, and propose supplementing low-level deep neural network (DNN) features with locally computed radiomic features. This reduces DNN over-fitting and allows local variations between radiomic features to be better captured. Furthermore, we ensure complementary information is learned by deep and radiomic features by designing a novel feature de-correlation loss. Combined, our method addresses the limitations of deep learning and radiomic approaches and outperforms state-of-the-art radiomic, deep learning, and hybrid approaches, achieving 86.9% AUC for the AF sub-type classification task. Code is available at https://github.com/xmed-lab/RIDL.Comment: Accepted by MICCAI2

Genome-Wide Identification of WRKY Family Genes and Analysis of Their Expression in Response to Abiotic Stress in Ginkgo biloba L.

Ginkgo biloba is widely planted, and the extracts of leaves contain flavonoids, terpene esters and other medicinal active ingredients. WRKY proteins are a large transcription factor family in plants, which play an important role in the regulation of plant secondary metabolism and development, as well as the response to biotic and abiotic stress. In our study, we identified 40 genes with conserved WRKY motifs in the G. biloba genome and classified into groups I (groups I-N and -C), II (groups IIa, b, c, d, and e), and III, which include 12, 26, and 2 GbWRKY genes, respectively. Meanwhile, the expression patterns of 10 GbWRKY (GbWRKY2, GbWRKY3, GbWRKY5, GbWRKY7, GbWRKY11, GbWRKY15, GbWRKY23, GbWRKY29, GbWRKY31, GbWRKY32) under different tissue and abiotic stress conditions were analyzed. Under stress treatment, the expression patterns of 10 WRKY genes were changed. 10 ginkgo WRKY transcription factors were induced by ETH and SA, but there are two different induced response modes. The expression of 10 WRKY genes was inhibited under low temperature, high temperature and MeJA hormone induction. Most WRKY genes were up-regulated under the induction of high salt and ABA. GbWRKYs were differentially expressed in various tissues after abiotic stress and plant hormone treatments, thereby indicating their possible roles in biological processes and abiotic stress tolerance and adaptation. Our results provided insight into the genome-wide identification of GbWRKYs, as well as their differential responses to stresses and hormones. These data can also be utilized to identify potential molecular targets to confer tolerance to various stresses in G. biloba

Characterization and expression analysis of four members genes of flavanone 3-hydroxylase families from Chamaemelum nobile

Chamaemelum nobile is a traditional Chinese herbal medicine, whose secondary metabolites used in the pharmacology of Chinese medicine. Among them, the flavonoids have great research value. Flavanone 3-hydroxylase (F3H) is one of the core enzymes in the early steps of flavonoid biosynthesis. This study aimed to elucidate the structures, functions, and expression levels of F3H families from C. nobile. Four members of the F3H family were screened from C. nobile transcriptome data and performed bioinformatics analysis. Results showed that CnF3H1~4 had a high similarity with the other F3H plants, and all genes contained two conserved isopenicillin N synthase-like and oxoglutarate/iron-dependent dioxygenase domains. Further analysis revealed that the four CnF3H proteins contained some differences in binding sites. The results of secondary and 3-D structures displayed that the composition and proportion of the four CnF3H secondary structures were basically the same, and their 3D structures were consistent with the secondary structures. The phylogenetic tree displayed that CnF3H2, CnF3H3, and CnF3H4 were grouped with Asteraceae. The expression patterns of CnF3Hs in the roots, stems, leaves, and flowers of C. nobile were evaluated using the value of RPKM. The results indicated that CnF3Hs had significant difference in the expression of different tissues. Especially, CnF3H1~3 and CnF3H4 had the highest expression levels in the flowers and roots, respectively. Hence, CnF3Hs played a significant role in the flavonoid metabolism

Spectroscopy of a Tunable Moir\'e System with a Correlated and Topological Flat Band

Moir\'e superlattices created by the twisted stacking of two-dimensional crystalline monolayers can host electronic bands with flat energy dispersion in which interaction among electrons is strongly enhanced. These superlattices can also create non-trivial electronic band topologies making them a platform for study of many-body topological quantum states. Among the moir\'e systems realized to date, there are those predicted to have band structures and properties which can be controlled with a perpendicular electric field. The twisted double bilayer graphene (TDBG), where two Bernal bilayer graphene are stacked with a twist angle, is such a tunable moir\'e system, for which partial filling of its flat band, transport studies have found correlated insulating states. Here we use gate-tuned scanning tunneling spectroscopy (GT-STS) to directly demonstrate the tunability of the band structure of TDBG with an electric field and to show spectroscopic signatures of both electronic correlations and topology for its flat band. Our spectroscopic experiments show excellent agreement with a continuum model of TDBG band structure and reveal signatures of a correlated insulator gap at partial filling of its isolated flat band. The topological properties of this flat band are probed with the application of a magnetic field, which leads to valley polarization and the splitting of Chern bands that respond strongly to the field with a large effective g-factor. Our experiments advance our understanding of the properties of TDBG and set the stage for further investigations of correlation and topology in such tunable moir\'e systems.Comment: 13 pages, 5 figures and supplementary informatio