This work proposes a Few-shot network employed for Perirenal Fat and Renal Parenchyma Semantic Segmentation (PRS-Net) in Diabetic Kidney Disease (DKD). PRS-Net integrates ST module and Fusion module. These two modules allow the model to process CT images with different spatial distributions and fuse multi-scale features, thereby enhancing performance in image segmentation. Utilizing the Perirenal Fat and Renal Parenchyma Dataset for semantic segmentation, PRS-Net achieves a mean intersection over union of 60.22% on test set, achieving superior performance compared to the other models. PRS-Net has clinical significance for early DKD diagnosis.

Metabolic Syndrome (MetS) is a prevalent condition associated with an increased risk of cardiovascular diseases, characterized by high blood pressure, hyperglycemia, and dyslipidemia. These risk factors not only exacerbate cardiovascular conditions but also impair immune function. Timely detection and prevention of MetS are imperative to mitigate these health risks. Recent advancements indicate various diagnostic approaches, including electrochemical biomarker detection, muscle mass to visceral fat ratio assessments, and anthropometric indices such as the body roundness index. Moreover, multimodality imaging techniques have become essential tools in comprehensive evaluation of MetS. This study introduces a cost-effective infrared thermal imager designed for MetS ordinary monitoring. Through simulation experiments involving 20 participants, 400 images of samples are collected and analyzed. The results demonstrate significant differences in thermal images between negative and positive samples. This innovative method could potentially offer a cost-effective and non-invasive tool for MetS monitoring.

Cervical cancer poses a significant threat to the health of women worldwide. Cervical cytopathology screening is an effective method for diagnosing cervical cancer. However, manual screening is time-consuming and prone to errors. The advent of automatic Computer-Aided Diagnosis (CAD) systems based on deep learning addresses this problem, however training these models requires large amounts of labeled data, which may not always be available. This paper proposes a Few-Shot Learning (FSL) framework called FSL-DSC to perform cervical cell classification tasks on small dataset. FSL-DSC first proposes inner loop learning and outer loop learning for individual tasks and overall parameter updates respectively, then a depthwise separable module is designed to further enhance the performance of the model. Among three repeated experiments, the FSL-DSC framework achieves an average accuracy of 83.34%, which shows the effectiveness and potential of the proposed FSL-DSC in the field of cervical image classification and few-shot tasks.

Smartphone-based indoor positioning and navigation remains a challenging task, as specialized technologies such as ultra-wideband (UWB) or Wi-Fi fine-time measurement are still niche and supported by only a few flagship smartphones. Therefore, standard technologies based on RSSI measurements, mainly Bluetooth Low Energy (BLE) and Wi-Fi, are used to obtain absolute positioning information of pedestrians inside buildings. Sensor fusion methods combine this with relative information from modeling human movement using sensor data provided by the smartphone’s IMU. It is also common practice to restrict this movement to the actual accessible areas of the building (e.g. restricting moving through walls), using spatial models based on the building’s floor plan. Without further assumptions, this complexity inevitably leads to a non-linear and non-Gaussian state space model. A common tool for (position) estimation in such scenarios is the broad class of particle filters. However, the use of such spatial constraints accelerates the well-known problem of sample impoverishment, which in the worst case can lead to the particle filter completely losing track, getting stuck and never recovering. This work begins with a brief presentation of an award-winning Indoor Positioning System (IPS) derived from previous work. Based on this, we present several approaches using Particle Distribution Optimization (PDO) that attempt to solve the impoverishment problem and ultimately lead to better overall positioning results. In the experiments, we compare them in two different buildings under realistic conditions and discuss the results in detail.

In the medical field, on the one hand, data mining can effectively establish evaluation models to supplement gold standards; on the other hand, it can guide the direction of scientific research by establishing connections between knowledge. Radiology images and pathological images are considered to be the most suitable medical data for data mining due to their large amount of information. Endometrial carcinoma is a common malignant tumor in women, and TP53 mutation status is an important factor affecting the occurrence and development of tumors. In this study, we propose a neural network structure based on multi-modal medical data that can predict TP53 mutations in endometrial carcinoma, with an accuracy of 86.21% in test set. Then, we clustered TP53-related deep learning features, and we believe that there is heterogeneity in TP53-related deep learning features.

For esophageal cancer immunotherapy, Programmed Death Ligand-l (PD-LI) is considered a predictive biomarker. However, immunehistochemistry (IHC) methods used to quantify PD-LI are challenged by high cost, time and variability. In contrast, hematoxylin and eosin (H&E) staining is a reliable method commonly used in cancer diagnosis. By employing advanced deep learning techniques, this study demonstrates the feasibility of predicting PD-LI expression from H&E stained images. With the help of pathologists, a dataset is constructed to evaluate the validity of PD-LI prediction in esophageal cancer by H&E using the FusedNet model. In 227 patients, PD-LI status is systematically predicted. Consistent prediction performance is demonstrated through validation of the validation set, proving that the system can be used as a decision support and quality assurance system in clinical practice.

Background and purpose: Skin tumours have become one of the most common diseases worldwide. While benign ones are not usually a threat to human health, malignant ones can develop into skin cancer and become life-threatening if left untreated. Early detection of the disease is important for the treatment of patients with skin tumours and dermoscopy is the most effective means of diagnosing skin tumours. However, the complexity of skin tumour cells makes the diagnosis somewhat erroneous for doctors. Therefore, a dermoscopic classification network based on deep learning and computer-aided diagnostic techniques is needed to obtain a high diagnostic accuracy rate for skin tumours. Methods: In this paper, Deep-skin, a model for dermoscopic image classification is proposed, which is based on both attention mechanism and ensemble learning. Considering the characteristics of dermoscopic images, embedding different attention mechanisms on top of Inception-V3 has been suggested to obtain more potential features. We then improve the classification performance by late fusion of the different models. To demonstrate the effectiveness of Deep-skin, experiments and evaluations are performed on the publicly available dataset Skin Cancer: Malignant vs. Benign and compare the performance of Deep-skin with other classification models. Results: The experimental results indicate that Deep-skin performs well on the dataset in comparison to other models, achieving a maximum accuracy of 87.8%.Conclusion: In this paper, the Deep-skin model is proposed for the classification of dermoscopic images and has shown better performance. In the future, we intend to investigate better classification models for automatic diagnosis of skin tumours. Such models can potentially assist physicians and patients in clinical settings.

The detection of environmental microorganisms is always a difficult task, e specially when the multi-scale environment is complex. For tiny objects in microscopic images, current detection methods face the challenge of accurate identification and localization. In contrast, we propose a convolutional neural network (ECA-RetinaNet) for microscopic object detection of which underlying dataset is a high-quality EMDS-7 dataset. The accuracy of ECA-RetinaNet is high, with a high mean Average Precision (mAP) value of 81.42% in the Environmental Microorganisms (EMS) detection task. Its accuracy has been higher than that of the two-stage object detection network.

It is common practice to use a sample-based representation to solve problems having a probabilistic interpretation. In many real world scenarios one is then interested in finding a best estimate of the underlying problem, e.g. the position of a robot. This is often done by means of simple parametric point estimators, providing the sample statistics. However, in complex scenarios this frequently results in a poor representation, due to multimodal densities and limited sample sizes. Recovering the probability density function using a kernel density estimation yields a promising approach to solve the state estimation problem i. e. finding the “real” most probable state, but comes with high computational costs. Especially in time critical and time sequential scenarios, this turns out to be impractical. Therefore, this work uses techniques from digital signal processing in the context of estimation theory, to allow rapid computations of kernel density estimates. The gains in computational efficiency are realized by substituting the Gaussian filter with an approximate filter based on the box filter. Our approach outperforms other state of the art solutions, due to a fully linear complexity and a negligible overhead, even for small sample sets. Finally, our findings are evaluated and tested within a real world sensor fusion system.

Depth estimation is a method to estimate the depth information in a 2D image/video, where the original 3D space is projected onto an image plane. This paper introduces a novel extension of depth estimation in the video domain, where we extract 3D trajectories which individually represent the transition of an object in the 3D space. Such 3D trajectories are useful for appropriately characterising spatio-temporal object relations for video event detection. While we extract 3D trajectories by combining depth estimation and object detection results, the major problem is the inconsistency between these results. For example, significantly different depths may be estimated for the region of the same object, and an object region that is appropriately shaped by estimated depths may be missed. To overcome this, we first initialise the 3D position of an object using the frame with the highest consistency between the depth estimation and object detection results. Then, we track the object in the 3D space using particle filter, where a 3D position of the object is modelled as a hidden state to generate its 2D visual appearance. Experimental results demonstrate the effectiveness of our method.

This paper proposes a novel object matching algorithm based on shape contours. In order to ensure low computational complexity in shape representation, our descriptor is composed by a small number of interest points which are generated by considering both curvatures and the overall shape trend. To effectively describe each point of interest, we introduce a simple and highly discriminative point descriptor, namely Point Context, which represents its geometrical and topological location. For shape matching, we observed that the correspondences are not only dependent on the similarities between these single points in different objects, but they are also related to the geometric relations between multiple points of interest in the same object. Therefore, a high-order graph matching formulation is introduced to merge the single point similarities and the similarities between point triangles. The main contributions of this paper include (i) the introduction of a novel shape descriptor with robust shape points and their descriptors and (ii) the implementation of a high-order graph matching algorithm that solves the shape matching problem. Our method is validated through a series of object retrieval experiments on four datasets demonstrating its robustness and accuracy.

This paper introduces a method for Multimedia Event Detection (MED). Given training videos for a certain event, a classifier is constructed to identify videos displaying it. In particular, the problems of the weakly supervised setting and the unclear event structure are addressed in this paper. The first issue is associated with the loosely annotated training videos that usually contain many irrelevant shots. The second one is the difficulty of assuming the event structure in advance, because videos can be created by arbitrary camera and editing techniques. To overcome these problems, a Hidden Conditional Random Field (HCRF) is used where hidden states work as an intermediate layer to discriminate between relevant and irrelevant shots to the event. In addition, the relation among hidden states characterises the event structure. Thus, the above problems are managed by optimising hidden states and their relation, so as to distinguish videos where the event occurs from the rest of videos. Experimental results on TRECVID video data validate the effectiveness of HCRFs in this context.