Reviewerly/exHarmony huggingface dataset:
Extremely Weakly-Supervised Blood Vessel Segmentation with Physiologically Based Synthesis and Domain Adaptation

Accurate analysis and modeling of renal functions require a precise segmentation of the renal blood vessels. Micro-CT scans provide image data at higher resolutions, making deeper vessels near the renal cortex visible. Although deep-learning-based methods have shown state-of-the-art performance in automatic blood vessel segmentations, they require a large amount of labeled training data. However, voxel-wise labeling in micro-CT scans is extremely time-consuming, given the huge volume sizes. To mitigate the problem, we simulate synthetic renal vascular trees physiologically while generating corresponding scans of the simulated trees by training a generative model on unlabeled scans. This enables the generative model to learn the mapping implicitly without the need for explicit functions to emulate the image acquisition process. We further propose an additional segmentation branch over the generative model trained on the generated scans. We demonstrate that the model can directly segment blood vessels on real scans and validate our method on both 3D micro-CT scans of rat kidneys and a proof-of-concept experiment on 2D retinal images. Code and 3D results are available at ( https://github.com/diku-dk/RenalVesselSeg ).
Label-Efficient Contrastive Learning-Based Model for Nuclei Detection and Classification in 3D Cardiovascular Immunofluorescent Images

Recently, deep learning-based methods achieved promising performance in nuclei detection and classification applications. However, training deep learning-based methods requires a large amount of pixel-wise annotated data, which is time-consuming and labor-intensive, especially in 3D images. An alternative approach is to adapt weak-annotation methods, such as labeling each nucleus with a point, but this method does not extend from 2D histopathology images (for which it was originally developed) to 3D immunofluorescent images. The reason is that 3D images contain multiple channels (z-axis) for nuclei and different markers separately, which makes training using point annotations difficult. To address this challenge, we propose the Label-efficient Contrastive learning-based (LECL) model to detect and classify various types of nuclei in 3D immunofluorescent images. Previous methods use Maximum Intensity Projection (MIP) to convert immunofluorescent images with multiple slices to 2D images, which can cause signals from different z-stacks to falsely appear associated with each other. To overcome this, we devised an Extended Maximum Intensity Projection (EMIP) approach that addresses issues using MIP. Furthermore, we performed a Supervised Contrastive Learning (SCL) approach for weakly supervised settings. We conducted experiments on cardiovascular datasets and found that our proposed framework is effective and efficient in detecting and classifying various types of nuclei in 3D immunofluorescent images.
To Pretrain or Not to Pretrain? A Case Study of Domain-Specific Pretraining for Semantic Segmentation in Histopathology

Annotating medical imaging datasets is costly, so fine-tuning (or transfer learning) is the most effective method for digital pathology vision applications such as disease classification and semantic segmentation. However, due to texture bias in models trained on real-world images, transfer learning for histopathology applications might result in underperforming models, which necessitates the need for using unlabeled histopathology data and self-supervised methods to discover domain-specific characteristics. Here, we tested the premise that histopathology-specific pretrained models provide better initializations for pathology vision tasks, i.e., gland and cell segmentation. In this study, we compare the performance of gland and cell segmentation tasks with histopathology domain-specific and non-domain-specific (real-world images) pretrained weights. Moreover, we investigate the dataset size at which domain-specific pretraining produces significant gains in performance. In addition, we investigated whether domain-specific initialization improves the effectiveness of out-of-distribution testing on distinct datasets but the same task. The results indicate that performance gain using domain-specific pretrained weights depends on both the task and the size of the training dataset. In instances with limited dataset sizes, a significant improvement in gland segmentation performance was also observed, whereas models trained on cell segmentation datasets exhibit no improvement. Github Code Repository .
Reducing Manual Annotation Costs for Cell Segmentation by Upgrading Low-Quality Annotations

Deep learning algorithms for image segmentation typically require large data sets with high-quality annotations to be trained with. For many domains, the annotation cost for obtaining such sets may prove to be prohibitively expensive. Our work aims to reduce the time necessary to create high-quality annotated images by using a relatively small well-annotated data set for training a convolutional neural network to upgrade lower-quality annotations, produced at lower annotation costs. We apply our method to the task of cell segmentation and investigate the performance of our solution when upgrading annotation quality for labels affected by three types of annotation errors: omission, inclusion, and bias. We observe that our method is able to upgrade annotations affected by high error levels from 0.3 to 0.9 Dice similarity with the ground-truth annotations. Moreover, we show that a relatively small well-annotated set enlarged with samples with upgraded annotations can be used to train better-performing segmentation networks compared to training only on the well-annotated set.
Active Transfer Learning for 3D Hippocampus Segmentation

Insufficient data is always a big challenge for medical imaging that is limited by the expensive labeling cost, time-consuming and intensive labor. Active learning aims to reduce the annotation effort by training a model on actively selected samples, most of them adopt uncertainty measures as instance selection criteria. However, uncertainty strategies underperform in most active learning studies. In addition, inaccurate selections worse than random sampling in initial stage referred to as “cold start” problem is also a huge challenge for active learning. Domain adaptation aims at alleviating the cold start problem and also reducing the annotation effort by adapting the model from a pre-trained model trained on another domain. Our work focuses on whether active learning can benefit from domain adaptation and the performance of uncertainty strategy compared to random selection. We studied 3D hippocampus images segmentation based on 3D UX-Net and four MRI datasets Hammers, HarP, LPBA40, and OASIS. Our experiments reveal that active learning with domain adaptation is more efficient and robust than without domain adaptation at a low labeling budget. The performance gap between them diminishes as we approach to that half of the dataset is labeled. In addition, entropy sampling also converges faster than random sampling, with slightly better performance.
Test-Time Augmentation-Based Active Learning and Self-training for Label-Efficient Segmentation

Deep learning techniques depend on large datasets whose annotation is time-consuming. To reduce annotation burden, the self-training (ST) and active-learning (AL) methods have been developed as well as methods that combine them in an iterative fashion. However, it remains unclear when each method is the most useful, and when it is advantageous to combine them. In this paper, we propose a new method that combines ST with AL using Test-Time Augmentations (TTA). First, TTA is performed on an initial teacher network. Then, cases for annotation are selected based on the lowest estimated Dice score. Cases with high estimated scores are used as soft pseudo-labels for ST. The selected annotated cases are trained with existing annotated cases and ST cases with border slices annotations. We demonstrate the method on MRI fetal body and placenta segmentation tasks with different data variability characteristics. Our results indicate that ST is highly effective for both tasks, boosting performance for in-distribution (ID) and out-of-distribution (OOD) data. However, while self-training improved the performance of single-sequence fetal body segmentation when combined with AL, it slightly deteriorated performance of multi-sequence placenta segmentation on ID data. AL was helpful for the high variability placenta data, but did not improve upon random selection for the single-sequence body data. For fetal body segmentation sequence transfer, combining AL with ST following ST iteration yielded a Dice of 0.961 with only 6 original scans and 2 new sequence scans. Results using only 15 high-variability placenta cases were similar to those using 50 cases. Code is available at: https://github.com/Bella31/TTA-quality-estimation-ST-AL .
Prediction of Au-Associated Minerals in Eastern Thailand Based on Stream Sediment Geochemical Data Analysis by S-A Multifractal Model

Conducted within the scope of geochemical exploration in eastern Thailand, this study aims to detect geochemical anomalies and potential mineral deposits. The objective was to interpret intricate spatial dispersion patterns and concentration levels of deposit pathfinder elements, specifically arsenic (As), copper (Cu), and zinc (Zn), using a comprehensive array of stream sediment geochemistry data. Methodologies involved integrating multifractal properties and traditional statistics, facilitated by the GeoDAS and ArcGIS platforms as instrumental analytical tools. In total, 5376 stream sediment samples were collected and evaluated, leading to the development of an in-depth geochemical map. The results indicated distinct geological units marked by substantially elevated average values of the aforementioned elements. Identification of geochemical anomalies was achieved through the spatial distribution method and the subsequent application of the spectrum-area (S-A) multifractal model. An intriguing link was found between high As concentrations and gold deposits in the area, suggesting As as a viable pathfinder element for gold mineralization. The anomaly maps, generated from the stream sediment data, spotlighted potential zones of interest, offering valuable guidance for future mineral exploration and geological inquiries. Nonetheless, it is vital to recognize that the increased values noted in these maps may be influenced by regional geological factors, emphasizing the necessity for a diverse set of analytical methods for accurate interpretation. This study’s significance lies in its pioneering use of the S-A multifractal model in geochemical data analysis. This innovative approach has deepened our comprehension of geochemical dispersion patterns and improved the precision of mineral exploration.
About e-gH modules

This article introduced and explored the concept of the -gH module and its relation to many other module types.
Contrast Learning Based Robust Framework for Weakly Supervised Medical Image Segmentation with Coarse Bounding Box Annotations

The shortage of data due to high annotation costs has limited the development of supervised medical image segmentation methods that rely on tight pixel-level annotations. Recently, weakly supervised methods based on multiple instance learning have been proposed to reduce the annotation cost by using bounding box annotations and achieve competitive performance. However, most existing methods require accurate bounding box annotations to generate positive and negative sample bags, which is difficult to realize due to the inevitable errors associated with manually annotated bounding boxes. In this study, we propose a robust framework based on contrast learning for weakly supervised medical image segmentation. Specifically, our method involves a Fine-grained Semantic Representation Module (FSRM), which is used to distinguish foreground and background pixels inside a coarse bounding box. Positive and negative sample bags are generated for multiple instance learning based on the obtained foreground results instead of bounding box constraints. Therefore, our proposed method can ensure the performance under coarse labeling by automatically extracting the boundaries of foreground and background. Our method achieves state-of-the-art results on two publicly available datasets, and extensive experiments validate the robustness of our method under noisy annotations. The source code will be available at https://github.com/ta1ly/wsis-contrastlearning .
Understanding Consumer’s Willingness to Engage with Digital Reuse Systems

Reusable packaging systems can help tackle the global problem of plastic waste by keeping material in circulation. Furthermore, technology now makes it possible to trace material as it moves through the reuse process, which potentially confers benefits across the supply chain. However, research into these digital reuse systems remains limited, particularly from the perspective of consumers who are asked to use such systems. The current work involved a series of online studies designed to identify (i) the barriers and enablers to engaging with digital reuse systems, (ii) which are most strongly endorsed and predict willingness to engage, and (iii) what information about digital reuse systems influences consumers’ willingness to engage and their beliefs. Studies 1 (N = 40) and 2 (N = 300) found that, while people are typically willing to engage with digital reuse systems, some people have concerns over the tracking aspect of digital reuse systems, and this is associated with their willingness to engage. Study 3 (N = 690) found that providing consumers with different types of information about digital reuse systems (e.g., going into more vs. less detail) did not influence their willingness to engage with digital reuse systems or their beliefs about the systems. These findings have implications for how digital reuse systems should be implemented.
Object Detection of UAV Images from Orthographic Perspective Based on Improved YOLOv5s

Object detection methods of UAV (Unmanned Aerial Vehicle) images are greatly improved with the development of UAV technology. In comparison, the existing object detection methods of UAV images lack outstanding performance in the face of challenges such as small targets, dense scenes, sparse distribution, occlusion, and complex background, especially prominent in the task of vehicle detection. This paper proposed an improved YOLOv5s method to perform vehicle detection of UAV images. The CA (Coordinate Attention) is first applied to the neck of YOLOv5s to generate direction-aware and position-sensitive feature maps, respectively, to improve the detection accuracy of sparsely distributed vehicle targets in complex backgrounds. Then, an improved PAFPN (Path Aggregation Feature Pyramid Network) at the neck of YOLOv5s is proposed for more efficient detection of small and dense vehicle targets. Finally, the CIoU (Complete Intersection Over Union) loss function was used to calculate the bounding box regression to obtain a more comprehensive overlap measure to accommodate different shapes of vehicle targets. We conducted extensive experiments on the self-built UAV-OP (Unmanned Aerial Vehicle from Orthographic Perspective) dataset. The experimental results show that our method achieves the best detection accuracy with a small quantity of calculation increase compared with YOLOv5s. The mAP50 improved by 3%, and the mAP50:95 improved by 1.7% with a 0.3 GFlops increase.
G-CNN: Adaptive Geometric Convolutional Neural Networks for MRI-Based Skull Stripping

Skull stripping in MRI-based brain imaging involves extraction of brain regions from raw images. While some Convolutional Neural Nets (CNNs) models have been successful in automating this process, the reliance on local textures can negatively impact model performance in the presence of pathological conditions such as brain tumors. This study presents a novel yet practical approach to offer supplementary texture-invariant spatial information of the brain as a geometric prior to enhance shape representations to further enable informed segmentation decisions. Our numerical results demonstrate that the new method outperforms competing algorithms in skull stripping tasks for both healthy and pathological inputs. These findings underscore the potential of incorporating geometric information in deep learning models to enhance the accuracy of brain segmentation.
Fully Convolutional Transformer-Based GAN for Cross-Modality CT to PET Image Synthesis

Positron emission tomography (PET) imaging is widely used for staging and monitoring the treatment of lung cancer, but the expensive cost of PET imaging equipment and the numerous contraindications for the examination present significant challenges to individuals and institutions seeking PET scans. Cross-modality image synthesis could alleviate this problem, but existing method still have deficiencies. Such as, pix2pix mode has stringent data requirements, while cycleGAN mode, although it can address this issue, does not produce a unique optimal solution. Additionally, models with convolutional neural network backbone still exhibit limitations when dealing with medical images containing contextual relationships between healthy and pathological tissues. In this paper, we propose a generative adversarial network (GAN) method based on a fully convolutional transformer and residual blocks called C2P-GAN for cross-modality synthesis of PET images from CT images. It composed of a generator and a discriminator that compete with each other, as well as a registration network that can eliminate noise interference. The generator integrates convolutional networks that excel in capturing local image features with the transformer that is sensitive to global contextual information. In the current dataset of 23 pairs of lung cancer patients collected, quantitative and qualitative experimental results demonstrate the superiority of the proposed method relative to competing methods and have great potential for clinical applications.
Effects of wind speed and temperature on economics and environmental impact assessment of different solar PV systems in Malaysia

This study aims to analyse the effect of temperature and wind speed on the performance of different types of photovoltaic (PV) systems at a different state in Malaysia and how it affects the economics and environmental impact assessment in the present year of 2018 as well as future years in 2030 and 2040. Three types of grid-connected solar PV modules namely Mono-Crystalline, Poly-crystalline, and Thin Film were selected to be implemented in different cities of Shah Alam, Chuping, Alor Setar, Ipoh and Kota Kinabalu. A mathematical model was adopted to estimate the performance characteristics of the solar PV modules. Based on the data in the present year, the highest power output produced by the Mc-Si module for Alor Setar city is given by 33.40 MWh/year while the lowest amount of power output provided by this PV panel is about 28.18 MWh/year in Shah Alam. Furthermore, an area of 144 m2 for the Mono-Crystalline PV module can satisfy the total energy requirement by the resident as it was the most profitable to be implemented compared to Poly-crystalline and Thin Film. The findings of this study can serve as important information on the economic viability of installing PV systems in the selected cities in Malaysia.
Text classification by CEFR levels using machine learning methods and BERT language model

This paper presents a study of the problem of automatic classification of short coherent texts (essays) in English according to the levels of the international CEFR scale. Determining the level of text in natural language is an important component of assessing students knowledge, including checking open tasks in e-learning systems. To solve this problem, vector text models were considered based on stylometric numerical features of the character, word, sentence structure levels. The classification of the obtained vectors was carried out by standard machine learning classifiers. The article presents the results of the three most successful ones: Support Vector Classifier, Stochastic Gradient Descent Classifier, LogisticRegression. Precision, recall and F-score served as quality measures. Two open text corpora, CEFR Levelled English Texts and BEA-2019, were chosen for the experiments. The best classification results for six CEFR levels and sublevels from A1 to C2 were shown by the Support Vector Classifier with F-score 67 % for the CEFR Levelled English Texts. This approach was compared with the application of the BERT language model (six different variants). The best model, bert-base-cased, provided the F-score value of 69 %. The analysis of classification errors showed that most of them are between neighboring levels, which is quite understandable from the point of view of the domain. In addition, the quality of classification strongly depended on the text corpus, that demonstrated a significant difference in F-scores during application of the same text models for different corpora. In general, the obtained results showed the effectiveness of automatic text level detection and the possibility of its practical application.
Traffic Police Dynamic Gesture Recognition Based on Spatiotemporal Attention ST-GCN

In order to solve the problems of low accuracy and poor robustness of traffic police dynamic gesture recognition, a ST-GCN based traffic police dynamic gesture recognition method is designed in this paper. The skeletal features of traffic police were extracted from space and time dimensions. By updating the graph attention matrix, the skeletal connectivity structure of traffic police was optimized to highlight the effective spatial features. Increase the time attention mechanism and strengthen the characteristics of the core movements in the learning gestures. The design model compares 8 types of gestures of traffic police in different scenarios, and the results show that: The ST-GCN traffic police gesture recognition network, which integrates spatio-temporal attention mechanism, achieves good recognition effect, with an average recognition accuracy of 88.82%, which is 5.79% higher than that of classical ST-GCN, which verifies the good performance of the proposed algorithm in traffic police dynamic gesture recognition.
Texture recognition under scale and illumination variations

Visual scene recognition is predominantly based on visual textures representing an object's material properties. However, the single material texture varies in scale and illumination angles due to mapping an object's shape. We present a comparative study of the colour histogram, Gabor, opponent Gabor, Local Binary Pattern (LBP), and wide-sense Markovian textural features concerning their sensitivity to simultaneous scale and illumination variations. Due to their application dominance, these textural features are selected from more than 50 published textural features. Markovian features are information preserving, and we demonstrate their superior performance for scale and illumination variable observation conditions over the standard alternative textural features. We bound the scale variation by double size, and illumination variation includes illumination spectra, acquisition devices, and 35 illumination directions spanned above a sample hemisphere. Recognition accuracy is tested on textile patterns from the University of East Anglia and wood veneers from UTIA BTF databases.