Multi-instance Learning for Bipolar Disorder Diagnosis using Weakly Labelled Speech Data

HCC(Hepatocellular carcinoma) is one of the primary liver cancer around the world, and it produces a high mortality rate in clinical. Surgical resection is the first-line treatment choice for patients with HCC, but the problem is patients will still have a great chance of recurrence after surgery. Therefore postoperative early recurrence prediction of HCC patients is necessary, which can guide physicians to manage the individualized follow-up so that we could increase the survival time of patients. MIL(multi-instance learning) model is mostly utilized in histopathological image analysis, and it has achieved promising results in most tasks, but it has one major limitation: the conventional MIL model loses the histopathological spatial structure information when generating the slide-level representation on flattened instance embeddings. In this light, we revisit the conventional MIL model and propose an improved MIL model to tackle the limitation. Our proposed method consists of three stages, the first stage is tissue patching, which is the same as the conventional MIL; the second stage is early recurrence probability heatmaps(ER-ProbMaps) generation, which is conducted by a well-trained patch-level prediction model on the tissue patches of the same slide; the final stage is to produce the slide-level recurrence prediction by a slide-level prediction model. We train the slide-level prediction model on obtained heatmaps to complete our postoperative early recurrence prediction task. Our experimental results show that our proposed improved MIL model can achieve superior performance than the conventional MIL model in our task.

COVID-19 pandemic has indeed plunged the global community especially African countries into an alarming difficult situationculminating into a great deal amounts of catastrophes such as economic recession, political instability and loss of jobs. Thepandemic spreads exponentially and causes loss of lives. Following the outbreak of the omicron new variant of concern,forecasting and identification of the COVID-19 infection cases is very vital for government at various levels. Hence, havingknowledge of the spread at a particular point in time, swift actions can be taken by government at various levels with a view toaccordingly formulate new policies and modalities towards minimizing the trajectory of the consequences of COVID-19 pandemicto both public health and economic sectors. Here, a potent combination of Convolutional Neural Network (CNN) learning algorithm along with Long Short Term Memory(LSTM) learning algorithm has been proposed in this work in order to produce a hybrid of a deep learning algorithm ConvolutionalNeural Network - Long Short Term Memory (CNN-LSTM) for forecasting COVID-19 infection cases particularly in Nigeria, SouthAfrica and Botswana. Forecasting models for COVID-19 infection cases in Nigeria, South Africa and Botswana, were developedfor 10 days using deep learning-based approaches namely CNN, LSTM and CNN-LSTM deep learning algorithm respectively. The models were evaluated on the basis of four standard performance evaluation metrics which include accuracy, MSE, MAEand RMSE respectively. However, the CNN-LSTM deep learning-based forecasting model achieved the best accuracy of98.30%, 97.60%, and 97.74% for Nigeria, South Africa and Botswana respectively; and in the same manner, achieved lesserMSE, MAE and RMSE values compared to models developed with CNN and LSTM respectively. Taken together, the CNN-LSTM deep learning-based forecasting model for COVID-19 infection cases in Nigeria, South Africaand Botswana dramatically surpasses the two other DL based forecasting models (CNN and LSTM) for COVID-19 infectioncases in Nigeria, South Africa and Botswana in terms of not only the best accuracy of with 98.30%, 97.60%, and 97.74% but alsoin terms of lesser MSE, MAE and RMSE.

Core-needle biopsy (CNB) plays a vital role in the initial diagnosis of breast cancer. However, the complex tissue processing and global shortage of pathologists have hindered traditional histopathology from timely diagnosis on fresh biopsies. In this work, we developed a full digital platform by integrating label-free stimulated Raman scattering (SRS) microscopy with weakly-supervised learning for rapid and automated cancer diagnosis on un-labelled breast CNB.Methods: We first compared the results of SRS imaging with standard hematoxylin and eosin (H&E) staining on adjacent frozen tissue sections. Then fresh unprocessed biopsy tissues were imaged by SRS to reveal diagnostic histoarchitectures. Next, weakly-supervised learning, i.e., the multi-instance learning (MIL) model was conducted to evaluate the ability to differentiate between benign and malignant cases, and compared with the performance of supervised learning model. Finally, gradient-weighted class activation mapping (Grad-CAM) and semantic segmentation were performed to spatially resolve benign/malignant areas with high efficiency.Results: We verified the ability of SRS in revealing essential histological hallmarks of breast cancer in both thin frozen sections and fresh unprocessed biopsy, generating histoarchitectures well correlated with H&E staining. Moreover, we demonstrated that weakly-supervised MIL model could achieve superior classification performance to supervised learnings, reaching diagnostic accuracy of 95% on 61 biopsy specimens. Furthermore, Grad-CAM allowed the trained MIL model to visualize the histological heterogeneity within the CNB.Conclusion: Our results indicate that MIL-assisted SRS microscopy provides rapid and accurate diagnosis on histologically heterogeneous breast CNB, and could potentially help the subsequent management of patients.

Despite a marked increase in treatment for bipolar disorder among youths, little is known about their pattern of service use. This article describes mental health service use in the year before and after a new clinical diagnosis of bipolar disorder. Claims were reviewed between April 1, 2004, and March 31, 2005, for 1,274,726 privately insured youths (17 years and younger) who were eligible for services at least one year before and after a service claim; 2,907 youths had new diagnosis of bipolar disorder during this period. Diagnoses of other mental disorders and prescriptions filled for psychotropic drugs were assessed in the year before and after the initial diagnosis of bipolar disorder. The one-year rate of a new diagnosis of bipolar disorder was .23%. During the year before the new diagnosis of bipolar disorder, youths were commonly diagnosed as having depressive disorder (46.5%) or disruptive behavior disorder (36.7%) and had often filled a prescription for an antidepressant (48.5%), stimulant (33.0%), mood stabilizer (31.8%), or antipsychotic (29.1%). Most youths with a new diagnosis of bipolar disorder had only one (28.8%) or two to four (28.7%) insurance claims for bipolar disorder in the year starting with the index diagnosis. The proportion starting mood stabilizers after the index diagnosis was highest for youths with five or more insurance claims for bipolar disorder (42.1%), intermediate for those with two to four claims (24.2%), and lowest for those with one claim (13.8%). Most youths with a new diagnosis of bipolar disorder had recently received treatment for depressive or disruptive behavior disorders, and many had no claims listing a diagnosis of bipolar disorder after the initial diagnosis. The service pattern suggests that a diagnosis of bipolar disorder is often given tentatively to youths treated for mental disorders with overlapping symptom profiles and is subsequently reconsidered.

Accurate and effective classification of plant diseases is critical for increasing yield and quality in agricultural production, minimizing economic losses through early detection of diseases, and implementing sustainable agriculture approaches. In this study, an approach for the detection and classification of plant leaf diseases is presented. We compare the performance of machine learning and deep learning-based models, and we use GAN-based data synthesis methods on a dataset we created to improve the model performance. For machine learning methods, ResNet-based feature extraction is performed and XGBoost, Random Forest, SVM, and InceptionV3 models are evaluated. In contrast, AlexNet, VGG16, VGG19, DenseNet, and ResNet models are examined within the scope of deep learning. The study was analyzed in three classes Phytophthora Infestans, Potassium Deficiency, and Healthy, and tested on data obtained from 21 different plant species. According to the model performances obtained, the deep learning-based ResNet model showed the highest success in all performance metrics and achieved 99% accuracy, showing superior performance compared to other methods. In the study, a comprehensive evaluation of multiple classification, GAN-based data synthesis, machine learning, and deep learning models was carried out. A valuable contribution was made to the existing studies in the literature.

To identify the feasibility of deep learning-based diagnostic models for detecting and assessing lower-extremity fatigue fracture severity on plain radiographs. This retrospective study enrolled 1151 X-ray images (tibiofibula/foot: 682/469) of fatigue fractures and 2842 X-ray images (tibiofibula/foot: 2000/842) without abnormal presentations from two clinical centers. After labeling the lesions, images in a center (tibiofibula/foot: 2539/1180) were allocated at 7:1:2 for model construction, and the remaining images from another center (tibiofibula/foot: 143/131) for external validation. A ResNet-50 and a triplet branch network were adopted to construct diagnostic models for detecting and grading. The performances of detection models were evaluated with sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), while grading models were evaluated with accuracy by confusion matrix. Visual estimations by radiologists were performed for comparisons with models. For the detection model on tibiofibula, a sensitivity of 95.4%/85.5%, a specificity of 80.1%/77.0%, and an AUC of 0.965/0.877 were achieved in the internal testing/external validation set. The detection model on foot reached a sensitivity of 96.4%/90.8%, a specificity of 76.0%/66.7%, and an AUC of 0.947/0.911. The detection models showed superior performance to the junior radiologist, comparable to the intermediate or senior radiologist. The overall accuracy of the diagnostic model was 78.5%/62.9% for tibiofibula and 74.7%/61.1% for foot in the internal testing/external validation set. The deep learning-based models could be applied to the radiological diagnosis of plain radiographs for assisting in the detection and grading of fatigue fractures on tibiofibula and foot. • Fatigue fractures on radiographs are relatively difficult to detect, and apt to be misdiagnosed. • Detection and grading models based on deep learning were constructed on a large cohort of radiographs with lower-extremity fatigue fractures. • The detection model with high sensitivity would help to reduce the misdiagnosis of lower-extremity fatigue fractures.

A recommendation attack detection approach integrating CNN with Bagging

Frailty is a common and critical condition in elderly adults, which may lead to further deterioration of health. However, difficulties and complexities exist in traditional frailty assessments based on activity-related questionnaires. These can be overcome by monitoring the effects of frailty on the gait. In this paper, it is shown that by encoding gait signals as images, deep learning-based models can be utilized for the classification of gait type. Two deep learning models (a) SS-CNN, based on single stride input images, and (b) MS-CNN, based on 3 consecutive strides were proposed. It was shown that MS-CNN performs best with an accuracy of 85.1%, while SS-CNN achieved an accuracy of 77.3%. This is because MS-CNN can observe more features corresponding to stride-to-stride variations which is one of the key symptoms of frailty. Gait signals were encoded as images using STFT, CWT, and GAF. While the MS-CNN model using GAF images achieved the best overall accuracy and precision, CWT has a slightly better recall. This study demonstrates how image encoded gait data can be used to exploit the full potential of deep learning CNN models for the assessment of frailty.

Due to the irregular and sporadic nature of wildfires, continuous monitoring of large areas is required. Since geostationary satellite sensors can observe large areas with high temporal resolution, they are suitable for monitoring wildfires in real time. However, the threshold algorithm currently employed for satellite-based active fire detection has poor performance in sensors with low spatial resolution. In addition, the algorithm does not account for environmental conditions that affect wildfire detection, resulting in poor generalization performance for large areas. This study examines the viability of an adaptive active fire detection model by combining satellite and numerical model data with deep learning. A model for active fire detection was developed using commonly employed brightness temperature-related variables (key variables) and local environmental variables (sub variables). Key variables are the cross spectral and spatial differences between the MIR (central wavelength of 3.85 m) and 2 TIR (central wavelengths of 9.63 and 11.20 m) channels of the Advanced Himawari Imager (AHI). Sub variables include Solar zenith angle (SOZ) and satellite zenith angle (SAZ) of AHI, skin temperature (ST) and relative humidity (RH) of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5)-land data. Four processes (confidence, frequency, land cover, and continuity tests) were used to extract reference fire samples from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) active fire products. To consider the different properties of key and sub variables, a 2-way convolutional neural network (CNN) structure was developed. To evaluate the influence of environmental variables, a CNN model without sub variables was adopted as a control model. The 2-way CNN (recall of 0.86, precision of 0.96, and standard deviation of recall of 0.13) was more robust at five focus sites than the control CNN (recall of 0.82, precision of 0.97, and standard deviation of recall of 0.163). Despite having a lower spatial resolution than MODIS/VIIRS, 2-way CNN outperformed other satellite-based active fire products (MODIS, VIIRS, AHI, and Advanced Meteorological Imager) in terms of detection capacity. The control CNN demonstrated poor performance under certain environmental conditions (high RH, high SAZ, and transition time between day and night), but 2-way CNN mitigates this tendency. In particular, the use of RH improved detection sensitivity, and SAZ contributed to the spatial robustness. This study demonstrated the significance of environmental conditions in active fire detection and proposed a suitable CNN structure for this intent. Based on the findings of this study, higher-level adaptive active fire monitoring under diverse environmental conditions will be possible together with explainable artificial intelligence.

To increase the productivity, it is important to manage yield and reduce defects in the semiconductor industry. One of the efforts is to identify defect patterns and control the cause factors that affects the defects. Many engineers inspect the quality of each chip and check the defect pattern on the wafer bin maps. To get the accurate and consistent classification results regardless of the level for domain knowledge or experience of engineers, deep learning-based models have recently been studied. Since most previous studies aim to classify the single-type defect patterns, it is needed to consider the mixed-type defect patterns together. Also, they require a lot of labeled data to train the deep learning-based classification model. However, defects occur extremely rarely in actual manufacturing process. Therefore, the method securing the higher accuracy in a situation where enough labeled data are not given is needed. This paper proposes a deep convolutional generative adversarial network for wafer map synthesis (DCGAN-WS) which generates the mixed-type patterns by synthesizing the single-type pattern and adding the pixel-wise summation. To maintain the characteristics of the binary pixel of the wafer bin maps, a thresholding technique is added. MixedWM38 dataset is used for the experiments, and it was verified that the mixed-type patterns were synthesized well. It helps to construct more robust model for single-type pattern classification and to generate the mixed-type patterns that have not occurred before. In the future, it is expected that this model addresses the problem of the lack of labeled data for defect pattern classification models.

DEEP-EP: Identification of epigenetic protein by ensemble residual convolutional neural network for drug discovery

Coronavirus disease-19 (COVID-19) has rapidly spread all over the world. It is found that the low sensitivity of reverse transcription-polymerase chain reaction (RT-PCR) examinations during the early stage of COVID-19 disease. Thus, efficient models are desirable for early-stage testing of COVID-19 infected patients. Chest X-ray (CXR) images of COVID-19 infected patients have shown some bilateral changes. In this paper, deep transfer learning and a deep forest-based model are proposed to diagnose COVID-19 infection from CXR images. Initially, features of X-ray images are extracted using the well-known deep transfer learning model (i.e., ResNet101), which does not require tuning many parameters compared to the deep convolutional neural network (CNN). After that, the deep forest model is utilised to predict COVID-19 infected patients. The deep forest is based upon ensemble learning and requires a small number of hyper-parameters. Additionally, the proposed model is trained on a multi-class dataset that contains four different classes as COVID-19 (+), pneumonia, tuberculosis, and healthy patients. The comparisons are drawn among the proposed deep transfer learning and deep forest-based models, the competitive models. The obtained results show that the proposed model effectively diagnoses COVID-19 infection with an accuracy of 99.4%.

PurposeThis paper aims to present a deep learning–based surrogate model for fast multi-material topology optimization of an interior permanent magnet (IPM) motor. The multi-material topology optimization based on genetic algorithm needs large computational burden because of execution of finite element (FE) analysis for many times. To overcome this difficulty, a convolutional neural network (CNN) is adopted to predict the motor performance from the cross-sectional motor image and reduce the number of FE analysis.Design/methodology/approachTo predict the average torque of an IPM motor, CNN is used as a surrogate model. From the input cross-sectional motor image, CNN infers dq-inductance and magnet flux to compute the average torque. It is shown that the average torque for any current phase angle can be predicted by this approach, which allows the maximization of the average torque by changing the current phase angle. The individuals in the multi-material topology optimization are evaluated by the trained CNN, and the limited individuals with higher potentials are evaluated by finite element method.FindingsIt is shown that the proposed method doubles the computing speed of the multi-material topology optimization without loss of search ability. In addition, the optimized motor obtained by the proposed method followed by simplification for manufacturing is shown to have higher average torque than a reference model.Originality/valueThis paper proposes a novel method based on deep learning for fast multi-material topology optimization considering the current phase angle.

Advancement in the Internet of Medical Things (IoMT), along with machine learning, deep learning, and artificial intelligence techniques, initiated a world of possibilities in healthcare. It has an extensive range of applications: when connected to the Internet, ordinary medical devices and sensors can collect valuable data, deep learning, and artificial intelligence techniques utilize this data and give an insight of symptoms, trends and enable remote care. Recently, Covid-19 pandemic outbreak caused the death of a large number of people. This virus has infected millions of people, and still, the rate of infected people is increasing day by day. Researchers are endeavoring to utilize medical images and deep learning-based models for the detection of Covid-19. Various techniques have been presented that utilize X-Ray images of the chest for the detection of Covid-19. However, the importance of regional-based convolutional neural networks (CNNs) is currently confined. Thus, this research aimed to introduce an IoT-based deep learning framework for early assessment of Covid-19. This framework can reduce the working pressure of medical experts/radiologists and contribute to the pandemic control. A deep learning-based model, i.e., faster regions with CNNs (Faster-RCNN) with ResNet-101, is applied on X-Ray images of the chest for Covid-19 detection. It uses region proposal network (RPN) to perform detection. By employing the model, we achieve a detection accuracy of 98%. Therefore, we believe that the system might be capable in order to assist medical expert/radiologist, to verify early assessment toward Covid-19.

The increasing complexity and safety-critical nature of aircraft engines have made effective health monitoring and predictive maintenance essential in modern aviation systems. Turbofan engines operate under extreme conditions, where unexpected component degradation can lead to serious failures, high maintenance costs, and operational downtime. Traditional maintenance strategies, such as reactive and preventive maintenance, are no longer sufficient to handle these challenges efficiently. As a result, data-driven approaches based on machine learning and deep learning have gained significant attention in prognostics and health management (PHM) applications. This review focuses on health monitoring and remaining useful life (RUL) prediction of turbofan engines using advanced deep learning techniques. It examines how sensor data collected from aircraft engines can be utilized to detect degradation patterns and estimate future engine health. Particular emphasis is placed on hybrid deep learning models that combine feature extraction and temporal sequence learning, such as convolutional neural networks, autoencoders, and long short-term memory networks. The study also discusses publicly available benchmark datasets, especially the NASA C-MAPSS dataset, which is widely used for validating RUL prediction models. Key challenges such as data imbalance, noise, model interpretability, and real-time deployment are highlighted. Overall, this review demonstrates that deep learning-based prognostic models play a crucial role in improving prediction accuracy, enhancing aviation safety, and enabling reliable condition-based maintenance strategies for turbofan engines.