Uniform Selection And Representation Matching: A Framework For Classifying Wound Healing Stage.

We propose the Uniform Selection and Representation Matching framework, an image classification framework that leverages co-teaching, contrastive learning, representation matching, and uniform selection to perform accurate wound stage classification with limited and noisy-labeled data. Given that descriptors of wound stages are under-specified, making accurate recognition difficult, images that generate low classification confidence are identified using an entropy-based selection process. Pseudo-labels are assigned to the low-confidence images through the representation matching process, where images are embedded into latent space and labels are assigned through majority voting. The Uniform Selection and Representation Matching framework demonstrates high accuracy in classifying wound-stage images by achieving a classification accuracy of 90.0%, a significant improvement over conventional convolutional neural networks.Clinical relevance- This work proposes a wound-stage classification algorithm trained with minimal data and noisy labels. Applications include remotely monitoring wound healing, recommending treatments, and incorporating intelligent bandage devices.

Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis.

We propose a subject-aware contrastive learning deep fusion neural network framework for effectively classifying subjects' confidence levels in the perception of visual stimuli. The framework, called WaveFusion, is composed of lightweight convolutional neural networks for per-lead time-frequency analysis and an attention network for integrating the lightweight modalities for final prediction. To facilitate the training of WaveFusion, we incorporate a subject-aware contrastive learning approach by taking advantage of the heterogeneity within a multi-subject electroencephalogram dataset to boost representation learning and classification accuracy. The WaveFusion framework demonstrates high accuracy in classifying confidence levels by achieving a classification accuracy of 95.7% while also identifying influential brain regions.

WaveFusion Squeeze-and-Excitation: Towards an Accurate and Explainable Deep Learning Framework in Neuroscience.

We introduce WaveFusion Squeeze-and-Excite, a multi-modal deep fusion architecture, as a practical and effective framework for classifying and localizing neurological events. WaveFusion SE is composed of lightweight CNNs for per-lead time-frequency analysis and an attention network called squeeze and excitation network with a temperature factor for effectively integrating lightweight modalities for final prediction. Our proposed architecture demonstrates high accuracy in classifying subjects' anxiety levels with an overall accuracy of 97.53%, beating prior approaches by a considerable margin. As will also be demonstrated in the paper, our approach allows for real-time localization of neurological events during the inference without any additional post-processing. This is a great step towards an explainable DL framework for neuroscience applications.

An Effective Deep Learning Framework for Cell Segmentation in Microscopy Images.

Cell segmentation is a common step in cell behavior analysis. Reliably and automatically segmenting cells in microscopy images remains challenging, especially in differential inference contrast microscopy images and phase-contrast microscopy images. In this paper, we propose a deep learning solution combining a Mask RCNN architecture with Shape-Aware Loss to produce cell instance segmentation. Our approach outperforms prior works in cell segmentation, achieving an IOU of 91.91% on the DIC-C2DH-HeLa dataset and an IOU of 94.93 % on the PhC-C2DH-U373 dataset. Our framework can calculate cell instance segmentation masks from both types of microscopy images without any additional post-processing.Clinical Relevance - The proposed approach produces accurate instance segmentation in Differential Inference Contrast and Phase-Contrast microscopy images. The segmentation results can be reliably used in cell behavior analysis and cell tracking.

A quantitative evaluation of alcohol withdrawal tremors.

This paper evaluates the relation between Alcohol Withdrawal Syndrome tremors in the left and right hands of patients. By analyzing 122 recordings from 61 patients in emergency departments, we found a weak relationship between the left and right hand tremor frequencies (correlation coefficient of 0.63). We found a much stronger relationship between the expert physician tremor ratings (on CIWA-Ar 0-7 scale) of the two hands, with a correlation coefficient of 0.923. Next, using a smartphone to collect the tremor data and using a previously developed model for obtaining estimated tremor ratings, we also found a strong correlation (correlation coefficient of 0.852) between the estimates of each hand. Finally, we evaluated different methods of combining the data from the two hands for obtaining a single tremor rating estimate, and found that simply averaging the tremor ratings of the two hands results in the lowest tremor estimate error (an RMSE of 0.977). Looking at the frequency dependence of this error, we found that higher frequency tremors had a much lower estimation error (an RMSE of 1.102 for tremors with frequencies in the 3-6Hz range as compared to 0.625 for tremors with frequencies in the 7-10Hz range).

Simultaneous Measurement of Zinc, Copper, Lead and Cadmium in Baby Weaning Food and Powder Milk by DPASV.

Apart from the breast milk, infant formula and baby weaning food have a special role in infant diet. Infants and young children are very susceptible to amount of trace elements. Copper and zinc are two elements that add in infant food. Lead and cadmium are heavy metals that enter to food chain unavoidably. DPASV is a benefit and applicable method for measurement of trace elements in food products. In this study, concentration of zinc, copper, lead and cadmium in four brands of baby food (rice and wheat based) and powder milk was analyzed with DPASV and polarograph set. Total Mean ± SE of zinc, copper, lead and cadmium in baby foods (n = 240) were 11.86 ± 1.474 mg/100g, 508.197 ± 83.154 µg/100g, 0.445 ± 0.006, 0.050 ± 0.005 mg/Kg respectively. Also these amount in powder milk (n = 240) were 3.621± 0.529 mg/100g, 403.822 ± 133.953 µg/100g, 0.007 ± 0.003, 0.060 ± 0.040 mg/Kg respectively. Zinc level in baby food type I was higher than lablled value (P = 0.030), but in other brands was not difference. Concentration of copper in all of samples was in labeled range (P > 0.05). In each four products, level of lead and cadmium were lower than the standard limit (P < 0.05). Amount of zinc and lead in baby food I, had difference versus other products. Concentration of zinc, camium in baby food type I, was higher than type II (P = 0.043, 0.001 respectively). Concentration of lead and cadmium in baby food type II, was higher than infant formulas, but are in standard limit.