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Pain is an unpleasant feeling that can reflect a patient's health situation. Since measuring pain is subjective, time-consuming, and needs continuous monitoring, automated pain intensity detection from facial expression holds great potential for smart healthcare applications. Convolutional Neural Networks (CNNs) are recently being used to identify features, map and model pain intensity from facial images, delivering great promise in helping practitioners detect disease. Limited research has been conducted to determine pain intensity levels across multiple classes. CNNs with simple learning schemes are limited in their ability to extract feature information from images. In order to develop a highly accurate pain intensity estimation system, this study proposes a Deep CNN (DCNN) model using the transfer learning technique, where a pre-trained DCNN model is adopted by replacing its dense upper layers, and the model is tuned using painful facial. We conducted experiments on the UNBC-McMaster shoulder pain archive database to estimate pain intensity in terms of seven-level thresholds using a given facial expression image. The experiments show our method achieves a promising improvement in terms of accuracy and performance to estimate pain intensity and outperform the-state-of-the-arts models.

This paper presents a comprehensive review of retinal blood vessel segmentation based on deep learning. The geometric characteristics of retinal vessels reflect the health status of patients and help to diagnose some diseases such as diabetes and hypertension. The accurate diagnosis and timing treatment of these diseases can prevent global blindness of patients. Recently, deep learning algorithms have been rapidly applied to retinal vessel segmentation due to their higher efficiency and accuracy, when compared with manual segmentation and other computer-aided diagnosis techniques. In this work, we reviewed recent publications for retinal vessel segmentation based on deep learning. We surveyed these proposed methods especially the network architectures and figured out the trend of models. We summarized obstacles and key aspects for applying deep learning to retinal vessel segmentation and indicated future research directions. This article will help researchers to construct more advanced and robust models.

Neural networks have become deeper in recent years and this has improved its capacity to handle more complex tasks. However, deep neural network has more parameters and is easier to overfit, especially when training samples are insufficient. In this paper, we present a new regularization technique called batch contrastive regularization to improve generalization performance. The loss function is based on contrastive loss which enforces intra-class compactness and inter-class separability of batch samples. We explore three different contrastive losses: (1) the center contrastive loss which regularizes based on distances between data points and their corresponding class centroid, (2) the sample contrastive loss which is based on batch sample-pair distances, and (3) the multicenter loss which is similar to center contrastive loss except that the cluster centers are discovered from training. The proposed network has two heads, one for classification and the other for regularization. The regularization head is discarded during inference. We also introduce bag sampling to ensure that all classes in a batch are well represented. The performance of the proposed architecture is evaluated on the CIFAR-10 and CIFAR-100 datasets. Our experiments show that network regularized by batch contrastive loss display impressive generalization performance over a wide variety of classes, yielding more than 11% improvement for ResNet50 on CIFAR-100 when trained from scratch.

Automated segmentation of grey matter (GM) and white matter (WM) in gigapixel histopathology images is advantageous to analyzing distributions of disease pathologies, further aiding in neuropathologic deep phenotyping. Although supervised deep learning methods have shown good performance, its requirement of a large amount of labeled data may not be cost-effective for large scale projects. In the case of GM/WM segmentation, trained experts need to carefully trace the delineation in gigapixel images. To minimize manual labeling, we consider semi-surprised learning (SSL) and deploy one state-of-the-art SSL method (FixMatch) on WSIs. Then we propose a two-stage scheme to further improve the performance of SSL: the first stage is a self-supervised module to train an encoder to learn the visual representations of unlabeled data, subsequently, this well-trained encoder will be an initialization of consistency loss-based SSL in the second stage. We test our method on Amyloid-β stained histopathology images and the results outperform FixMatch with the mean IoU score at around 2% by using 6,000 labeled tiles while over 10% by using only 600 labeled tiles from 2 WSIs. CLINICAL RELEVANCE—: This work minimizes the required labeling efforts by trained personnel. An improved GM/WM segmentation method could further aid in the study of brain diseases, such as Alzheimer’s disease.

Dicrotic Notch (DN) is a distinctive and clinically significant feature of the arterial blood pressure curve. Its automatic identification has been the focus of many kinds of research using either model-based or rule-based methodologies. However, since DN morphology is quite variant following the patient-specific underlying physiological and pathological conditions, its automatic identification with these methods is challenging. This work proposes a hybrid approach that employs both model-based and rule-based approaches to enhance DN detection's generalizability. We have tested our approach on ABP data gathered from 14 pigs. Our result strongly indicates 36% overall mean error improvement with maximum 52% and -11% accuracy enhancement and degradation in extreme cases.

The need for manual and detailed annotations limits the applicability of supervised deep learning algorithms in medical image analyses, specifically in the field of pathology. Semi-supervised learning (SSL) provides an effective way for leveraging unlabeled data to relieve the heavy reliance on the amount of labeled samples when training a model. Although SSL has shown good performance, the performance of recent state-of-the-art SSL methods on pathology images is still under study. The problem for selecting the most optimal data to label for SSL is not fully explored. To tackle this challenge, we propose a semi-supervised active learning framework with a region-based selection criterion. This framework iteratively selects regions for annotation query to quickly expand the diversity and volume of the labeled set. We evaluate our framework on a grey-matter/white-matter segmentation problem using gigapixel pathology images from autopsied human brain tissues. With only 0.1% regions labeled, our proposed algorithm can reach a competitive IoU score compared to fully-supervised learning and outperform the current state-of-the-art SSL by more than 10% of IoU score and DICE coefficient.

We investigate the gate-controlled, electrically doped tunnelling current in Adenine-Thymine heterojunction nanotube-based Field Effect Transistor (FET). This analytical model FET is designed by Density Functional Theory (DFT) and Non-Equilibrium Green's Function (NEGF) based First principle formalisms. It is demonstrated that Band to Band Tunnelling (BTBT) is possible in relaxed Adenine-Thymine heterostructure nanotube. The evaluation of BTBT tunnelling probability to estimate tunnelling current for only ±0.01V applied bias voltage is calculated using Wentzel-Kramers-Brillouin approximation. Electrical doping is introduced to eliminate the probability of fault generation. By keen observation on the shift of energy levels in the band structure, the availability of high transmission co-efficient peaks and current-voltage response we demonstrate the Schottky barrier nature for this geometrically pre-optimized bio-molecular FET. The doping concentration is varied from 0.0001V to 0.1V to achieve a substantially large amount of tunnelling current when the electronic temperature is kept at 300K. The E-k diagram or complex band structure of this heterostructure nanotube ensures its in-direct semi-conducting nature. This is a first attempt to present a circuit-level demonstration using this Adenine-Thymine nanotube-based bio-molecular FET and validate the obtained results with the existing approaches.

Distracted driving is one of the leading causes of fatal road accidents. Current studies mainly use convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to classify distracted action through spatial and spectral information. Following the success application of transformer in natural language processing (NLP), transformer is introduced to handle computer vision tasks. Vision transformer can mine long-range relationship and less loss of information between layers. Compared to a regular vision transformer, a hierarchical transformer with representation computed with shifted windows could limit the self-attention computation, yielding more computation efficiency. In this work, we conduct a review on shifted-window hierarchical vision transformers, following the exact implementation of Swin Transformer in classifying distracted drivers through the American University in Cairo Distracted Driver Dataset (AUC-DDD). Results show that shifted-window hierarchical transformer can achieve a classification accuracy of 95.72% in distracted driver detection.

A large portion of crops are lost to plant diseases each year worldwide. In this study, a mobile application for detecting and classifying plant disease using deep learning object detection model was developed. The proposed mobile application utilizes Faster R-CNN object detector with Inception-v2 backbone network to achieve robust and efficient detection. Experiments on grape disease images demonstrated that the proposed application is able to achieve an accuracy of 97.9% while running solely on a smartphone without connecting to a server. The proposed mobile application can serve as an aid to farmers and crop growers who have little or no knowledge about plant diseases for early disease detection and control and therefore can reduce losses and prevent further spreading of the disease.