MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network

The inability to interpret the model prediction in semantically and visually meaningful ways is a well-known shortcoming of most existing computer-aided diagnosis methods. In this paper, we propose MDNet to establish a direct multimodal mapping between medical images and diagnostic reports that can read images, generate diagnostic reports, retrieve images by symptom descriptions, and visualize attention, to provide justifications of the network diagnosis process. MDNet includes an image model and a language model. The image model is proposed to enhance multi-scale feature ensembles and utilization efficiency. The language model, integrated with our improved attention mechanism, aims to read and explore discriminative image feature descriptions from reports to learn a direct mapping from sentence words to image pixels. The overall network is trained end-to-end by using our developed optimization strategy. Based on a pathology bladder cancer images and its diagnostic reports (BCIDR) dataset, we conduct sufficient experiments to demonstrate that MDNet outperforms comparative baselines. The proposed image model obtains state-of-the-art performance on two CIFAR datasets as well.Comment: CVPR2017 Ora

Novel image markers for non-small cell lung cancer classification and survival prediction

BACKGROUND: Non-small cell lung cancer (NSCLC), the most common type of lung cancer, is one of serious diseases causing death for both men and women. Computer-aided diagnosis and survival prediction of NSCLC, is of great importance in providing assistance to diagnosis and personalize therapy planning for lung cancer patients. RESULTS: In this paper we have proposed an integrated framework for NSCLC computer-aided diagnosis and survival analysis using novel image markers. The entire biomedical imaging informatics framework consists of cell detection, segmentation, classification, discovery of image markers, and survival analysis. A robust seed detection-guided cell segmentation algorithm is proposed to accurately segment each individual cell in digital images. Based on cell segmentation results, a set of extensive cellular morphological features are extracted using efficient feature descriptors. Next, eight different classification techniques that can handle high-dimensional data have been evaluated and then compared for computer-aided diagnosis. The results show that the random forest and adaboost offer the best classification performance for NSCLC. Finally, a Cox proportional hazards model is fitted by component-wise likelihood based boosting. Significant image markers have been discovered using the bootstrap analysis and the survival prediction performance of the model is also evaluated. CONCLUSIONS: The proposed model have been applied to a lung cancer dataset that contains 122 cases with complete clinical information. The classification performance exhibits high correlations between the discovered image markers and the subtypes of NSCLC. The survival analysis demonstrates strong prediction power of the statistical model built from the discovered image markers

Novel Image Markers for Non-Small Cell Lung Cancer Classification and Survival Prediction

BACKGROUND: Non-small cell lung cancer (NSCLC), the most common type of lung cancer, is one of serious diseases causing death for both men and women. Computer-aided diagnosis and survival prediction of NSCLC, is of great importance in providing assistance to diagnosis and personalize therapy planning for lung cancer patients. RESULTS: In this paper we have proposed an integrated framework for NSCLC computer-aided diagnosis and survival analysis using novel image markers. The entire biomedical imaging informatics framework consists of cell detection, segmentation, classification, discovery of image markers, and survival analysis. A robust seed detection-guided cell segmentation algorithm is proposed to accurately segment each individual cell in digital images. Based on cell segmentation results, a set of extensive cellular morphological features are extracted using efficient feature descriptors. Next, eight different classification techniques that can handle high-dimensional data have been evaluated and then compared for computer-aided diagnosis. The results show that the random forest and adaboost offer the best classification performance for NSCLC. Finally, a Cox proportional hazards model is fitted by component-wise likelihood based boosting. Significant image markers have been discovered using the bootstrap analysis and the survival prediction performance of the model is also evaluated. CONCLUSIONS: The proposed model have been applied to a lung cancer dataset that contains 122 cases with complete clinical information. The classification performance exhibits high correlations between the discovered image markers and the subtypes of NSCLC. The survival analysis demonstrates strong prediction power of the statistical model built from the discovered image markers

Clinical evaluation of the function of hypothalamo-pituitary-thyroid axis in children with central nervous system infections

<p>Abstract</p> <p>Background</p> <p>It is well known that certain non-thyroidal critical illness may lead to euthyroid sick syndrome(ESS). There are little reports about the change of thyroid hormone in the children's central nervous system (CNS) infections.</p> <p>Results</p> <p>The results of serum TT3, TT4 and TSH in these children were compared with those in 20 cases of healthy adults and 20 cases of adults with primary hypothyroidism. Serum T3 and T4 were decreased in 34/78 children with CNS infections, T3 and T4 were much lower than those of healthy adult (p < 0.05), but still higher than that of the primary hypothyroidism (p < 0.05), and TSH levels were not significant differences among children with CNS infections and children with non-CNS infections (p > 0.05).</p> <p>Low T3 and T4 levels in serum and cerebrospinal fluid(CSF)were predominant in children with serious infections of CNS, 31/34 (percent 91.17) cases of serious CNS infection had low serum TT3 and/or TT4. The low T3 with low T4 was seen in 14/34 children of severe CNS infections, 3 of them died. The levels of CSF T3 (X ± SD = 0.39 ± 0.47 ng/ml) and T4 (x ± SD = 1.02 ± 1.27 ug/dl) in the serious CNS infections were lower than that of non-CNS infections T3 (x ± SD = 0.93 ± 1.23 ng/ml), and T4 (x ± SD = 2.42 ± 1.70 ug/dl), 7 died children were all in the subjects of low T3 and/or low T4.</p> <p>In 22 children with non-CNS infections, serum T3 and T4 levels were lower than that of healthy adult, but have not significant difference(p > 0.05).</p> <p>Conclusions</p> <p>These results suggest that detection of TT3, TT4 and TSH in serum and/or CSF simultaneous or alone in analyses would be valuable in correctly judging thyroid function and evaluating the prognosis of the children with infections of CNS. Measuring a little amount of blood (1 ml)or CSF required for this method is a simple, convenient and accurate method.</p

Classification of temporal lobe epilepsy based on neuropsychological tests and exploration of its underlying neurobiology

ObjectiveTo assist improving long-term postoperative seizure-free rate, we aimed to use machine learning algorithms based on neuropsychological data to differentiate temporal lobe epilepsy (TLE) from extratemporal lobe epilepsy (extraTLE), as well as explore the relationship between magnetic resonance imaging (MRI) and neuropsychological tests.MethodsTwenty-three patients with TLE and 23 patients with extraTLE underwent neuropsychological tests and MRI scans before surgery. The least absolute shrinkage and selection operator were firstly employed for feature selection, and a machine learning approach with neuropsychological tests was employed to classify TLE using leave-one-out cross-validation. A generalized linear model was used to analyze the relationship between brain alterations and neuropsychological tests.ResultsWe found that logistic regression with the selected neuropsychological tests generated classification accuracies of 87.0%, with an area under the receiver operating characteristic curve (AUC) of 0.89. Three neuropsychological tests were acquired as significant neuropsychological signatures for the diagnosis of TLE. We also found that the Right-Left Orientation Test difference was related to the superior temporal and the banks of the superior temporal sulcus (bankssts). The Conditional Association Learning Test (CALT) was associated with the cortical thickness difference in the lateral orbitofrontal area between the two groups, and the Component Verbal Fluency Test was associated with the cortical thickness difference in the lateral occipital cortex between the two groups.ConclusionThese results showed that machine learning-based classification with the selected neuropsychological data can successfully classify TLE with high accuracy compared to previous studies, which could provide kind of warning sign for surgery candidate of TLE patients. In addition, understanding the mechanism of cognitive behavior by neuroimaging information could assist doctors in the presurgical evaluation of TLE

Ecological importance of cross-feeding of the intermediate metabolite 1,2-propanediol between bacterial gut symbionts

Cross-feeding based on the metabolite 1,2-propanediol has been proposed to have an important role in the establishment of trophic interactions among gut symbionts, but its ecological importance has not been empirically established. Here, we show that in vitro growth of Lactobacillus reuteri ATCC PTA 6475 is enhanced through 1,2-propanediol produced by Bifidobacterium breve UCC2003 and Escherichia coli MG1655 from the metabolization of fucose and rhamnose, respectively. Work with isogenic mutants showed that the tropic interaction is dependent on the pduCDE operon in L. reuteri, which encodes for the ability to use 1,2-propanediol, and the L-fucose permease (fucP) gene in B. breve, which is required for 1,2-propanediol formation from fucose. Experiments in gnotobiotic mice revealed that, although the pduCDE operon bestows a fitness burden on L. reuteri ATCC PTA 6475 in the mouse digestive tract, the ecological performance of the strain was enhanced in the presence of B. breve UCC2003 and the mucus-degrading species Bifidobacterium bifidum The use of the respective pduCDE and fucP mutants of L. reuteri and B. breve in the mouse experiments indicated that the trophic interaction was specifically based on 1,2-propanediol. Overall, our work established the ecological importance of cross-feeding relationships based on 1,2-propanediol for the fitness of a bacterial symbiont in the vertebrate gut.Importance Through experiments in gnotobiotic mice that employed isogenic mutants of bacterial strains that produce (Bifidobacterium breve) and utilize (Lactobacillus reuteri) 1,2-propanediol, this study provides mechanistic insight into the ecological ramifications of a trophic interaction between gut symbionts. The findings improve our understanding on how cross-feeding influences the competitive fitness of L. reuteri in the vertebrate gut and revealed a putative selective force that shaped the evolution of the species. The findings are relevant as they provide a basis to design rational microbial-based strategies to modulate gut ecosystems, which could employ mixtures of bacterial strains that establish trophic interactions or a personalized approach based on the ability of a resident microbiota to provide resources for the incoming microbe