Deep Convolutional Neural Network Ensembles Using ECOC

Deep neural networks have enhanced the performance of decision making systems in many applications, including image understanding, and further gains can be achieved by constructing ensembles. However, designing an ensemble of deep networks is often not very beneficial since the time needed to train the networks is generally very high or the performance gain obtained is not very significant. In this paper, we analyse an error correcting output coding (ECOC) framework for constructing ensembles of deep networks and propose different design strategies to address the accuracy-complexity trade-off. We carry out an extensive comparative study between the introduced ECOC designs and the state-of-the-art ensemble techniques such as ensemble averaging and gradient boosting decision trees. Furthermore, we propose a fusion technique, that is shown to achieve the highest classification performance

A thyrotropin‑secreting macroadenoma with positive growth hormone and prolactin immunostaining: A case report and literature review

Thyrotropin (thyroid stimulating hormone [TSH]) secreting pituitary adenomas (TSHoma) are rare adenomas presenting with hyperthyroidism due to impaired negative feedback of thyroid hormone on the pituitary and inappropriate TSH secretion. This article presents a case of TSH‑secreting macroadenoma without any clinical hyperthyroidism symptoms accompanying immunoreaction with growth hormone (GH) and prolactin. A 36‑year‑old female patient was admitted with complaints of irregular menses and blurred vision. On physical exam, she had bitemporal hemianopsia defect. Magnetic resonance imaging (MRI) evaluation showed suprasellar macroadenoma measuring 33 mm × 26 mm × 28 mm was detected on pituitary MRI. She had no hyperthyroidism symptoms clinically. Although free T4 and free T3 levels were elevated, TSH level was inappropriately within the upper limit of normal. Response to T3 suppression and thyrotropin releasing hormone‑stimulation test was inadequate. Other pituitary hormones were normal. Transsphenoidal adenomectomy was performed due to parasellar compression findings. Immunohistochemically widespread reaction was observed with TSH, GH and prolactin in the adenoma. The patient underwent a second surgical procedure 2 months later due to macroscopic residual tumor, bitemporal hemianopsia and a suprasellar homogenous uptake with regular borders on indium‑111 octreotide scintigraphy. After second surgery; due to ongoing symptoms and residual tumor, she was managed with octreotide and cabergoline treatment. On her follow‑up with medical treatment, TSH and free T4 values were within normal limits. Although silent TSHomas are rare, they may arise with compression symptoms as in our case. The differential diagnosis of secondary hyperthyroidism should include TSHomas and thyroid hormone receptor resistance syndrome.Key words: Inappropriate thyroid stimulating hormone, thyrotropin‑secreting pituitary adenoma, thyroid stimulating hormone adenom

A decision cognizant Kullback-Leibler divergence

In decision making systems involving multiple classifiers there is the need to assess classifier (in)congruence, that is to gauge the degree of agreement between their outputs. A commonly used measure for this purpose is the Kullback–Leibler (KL) divergence. We propose a variant of the KL divergence, named decision cognizant Kullback–Leibler divergence (DC-KL), to reduce the contribution of the minority classes, which obscure the true degree of classifier incongruence. We investigate the properties of the novel divergence measure analytically and by simulation studies. The proposed measure is demonstrated to be more robust to minority class clutter. Its sensitivity to estimation noise is also shown to be considerably lower than that of the classical KL divergence. These properties render the DC-KL divergence a much better statistic for discriminating between classifier congruence and incongruence in pattern recognition systems

ABCD Neurocognitive Prediction Challenge 2019: Predicting Individual Residual Fluid Intelligence Scores from Cortical Grey Matter Morphology

We predicted fluid intelligence from T1-weighted MRI data available as part of the ABCD NP Challenge 2019, using morphological similarity of grey-matter regions across the cortex. Individual structural covariance networks (SCN) were abstracted into graph-theory metrics averaged over nodes across the brain and in data-driven communities/modules. Metrics included degree, path length, clustering coefficient, centrality, rich club coefficient, and small-worldness. These features derived from the training set were used to build various regression models for predicting residual fluid intelligence scores, with performance evaluated both using cross-validation within the training set and using the held-out validation set. Our predictions on the test set were generated with a support vector regression model trained on the training set. We found minimal improvement over predicting a zero residual fluid intelligence score across the sample population, implying that structural covariance networks calculated from T1-weighted MR imaging data provide little information about residual fluid intelligence

A compact multifunctional microfluidic platform for exploring cellular dynamics in real-time using electrochemical detection

Downscaling of microfluidic cell culture and detection devices for electrochemical monitoring has mostly focused on miniaturization of the microfluidic chips which are often designed for specific applications and therefore lack functional flexibility. We present a compact microfluidic cell culture and electrochemical analysis platform with in-built fluid handling and detection, enabling complete cell based assays comprising on-line electrode cleaning, sterilization, surface functionalization, cell seeding, cultivation and electrochemical real-time monitoring of cellular dynamics. To demonstrate the versatility and multifunctionality of the platform, we explored amperometric monitoring of intracellular redox activity in yeast (Saccharomyces cerevisiae) and detection of exocytotically released dopamine from rat pheochromocytoma cells (PC12). Electrochemical impedance spectroscopy was used in both applications for monitoring cell sedimentation and adhesion as well as proliferation in the case of PC12 cells. The influence of flow rate on the signal amplitude in the detection of redox metabolism as well as the effect of mechanical stimulation on dopamine release were demonstrated using the programmable fluid handling capability. The here presented platform is aimed at applications utilizing cell based assays, ranging from e.g. monitoring of drug effects in pharmacological studies, characterization of neural stem cell differentiation, and screening of genetically modified microorganisms to environmental monitoring

Real-time monitoring of cellular dynamics using a microfluidic cell culture system with integrated electrode array and potentiostat

A versatile microfluidic, multichamber cell culture and analysis system with an integrated electrode array and potentiostat suitable for electrochemical detection and microscopic imaging is presented in this paper. The system, which allows on-line electrode cleaning and modification, was developed for real-time monitoring of cellular dynamics, exemplified in this work by monitoring of redox metabolism inside living yeast cells and dopamine release from PC12 cell