Open-source Datasets Research Articles

Construction of college student’s volleyball functional physical training system based on 5G embedded analysis from the perspective of biomechanics

In volleyball, the movement and performance of athletes are closely related to biomechanical principles. The process of getting the ball beyond the opponent’s net and landing on the court involves complex biomechanical actions such as jumping, spiking, and blocking. These actions require appropriate muscle activation, joint movement, and force generation. Through a 5G embedded physical training system, college students’ biomechanical performance in volleyball can be effectively monitored and analyzed. For example, the Wireless Sensor Network (WSN) can be used to measure the forces exerted on the body during movements, such as the impact force on the fingers during spiking and blocking, and the stress on the lower back. By analyzing these biomechanical data, problems such as finger injuries and lower back pain can be better understood and addressed. Moreover, the 5G embedded system allows for a detailed analysis of the biomechanical characteristics of different teams and players, enabling coaches and athletes to optimize training strategies. The system can also provide visual feedback on biomechanical parameters, which helps students improve their overall coordination and muscle strength. By incorporating biomechanical analysis into the volleyball physical training system, students can better understand the scientific basis of their movements and improve their performance. Statistical analysis has been carried out effectively based on the study analysis with the existing open-source datasets, providing a quantitative basis for biomechanical research and training optimization.

Lidar Simultaneous Localization and Mapping Algorithm for Dynamic Scenes

To address the issue of significant point cloud ghosting in the construction of high-precision point cloud maps by low-speed intelligent mobile vehicles due to the presence of numerous dynamic obstacles in the environment, which affects the accuracy of map construction, this paper proposes a LiDAR-based Simultaneous Localization and Mapping (SLAM) algorithm tailored for dynamic scenes. The algorithm employs a tightly coupled SLAM framework integrating LiDAR and inertial measurement unit (IMU). In the process of dynamic obstacle removal, the point cloud data is first gridded. To more comprehensively represent the point cloud information, the point cloud within the perception area is linearly discretized by height to obtain the distribution of the point cloud at different height layers, which is then encoded to construct a linear discretized height descriptor for dynamic region extraction. To preserve more static feature points without altering the original point cloud, the Random Sample Consensus (RANSAC) ground fitting algorithm is employed to fit and segment the ground point cloud within the dynamic regions, followed by the removal of dynamic obstacles. Finally, accurate point cloud poses are obtained through static feature matching. The proposed algorithm has been validated using open-source datasets and self-collected campus datasets. The results demonstrate that the algorithm improves dynamic point cloud removal accuracy by 12.3% compared to the ERASOR algorithm and enhances overall mapping and localization accuracy by 8.3% compared to the LIO-SAM algorithm, thereby providing a reliable environmental description for intelligent mobile vehicles.

Dilated dynamic supervised contrastive learning framework for fault diagnosis under imbalanced dataset conditions

Deep learning-based fault diagnosis methods have achieved significant results. However, under actual working conditions, the inability of equipment to operate for long periods in a fault state results in a much smaller amount of collected fault data compared to normal data. This leads to existing fault diagnosis models being able to learn only limited diagnostic knowledge, causing a substantial decline in diagnostic accuracy. Therefore, a dilated dynamic supervised contrast learning framework for imbalanced data scenarios is proposed. In this framework, a dilated convolution network is designed as a feature extractor to enhance the feature extraction ability by changing the dilation rate. Then, the dynamic supervision loss function is designed, and the sample compensation factor of each category is given according to the frequency of sample used in the training process to enhance the contribution of the minority class and optimize the feature extractor. Finally, a dynamic cross-entropy loss function is designed to train the network and classifier to achieve accurate classification of faults. Experiments on two open-source datasets show the effectiveness of the proposed model framework.

The Imaging Database for Epilepsy And Surgery (IDEAS).

Magnetic resonance imaging (MRI) is a crucial tool for identifying brain abnormalities in a wide range of neurological disorders. In focal epilepsy, MRI is used to identify structural cerebral abnormalities. For covert lesions, machine learning and artificial intelligence (AI) algorithms may improve lesion detection if abnormalities are not evident on visual inspection. The success of this approach depends on the volume and quality of training data. Herein, we release an open-source data set of pre-processed MRI scans from 442 individuals with drug-refractory focal epilepsy who had neurosurgical resections and detailed demographic information. We also share scans from 100 healthy controls acquired on the same scanners. The MRI scan data include the preoperative three-dimensional (3D) T1 and, where available, 3D fluid-attenuated inversion recovery (FLAIR), as well as a manually inspected complete surface reconstruction and volumetric parcellations. Demographic information includes age, sex, age a onset of epilepsy, location of surgery, histopathology of resected specimen, occurrence and frequency of focal seizures with and without impairment of awareness, focal to bilateral tonic-clonic seizures, number of anti-seizure medications (ASMs) at time of surgery, and a total of 1764 patient years of post-surgical followup. Crucially, we also include resection masks delineated from post-surgical imaging. To demonstrate the veracity of our data, we successfully replicated previous studies showing long-term outcomes of seizure freedom in the range of ~50%. Our imaging data replicate findings of group-level atrophy in patients compared to controls. Resection locations in the cohort were predominantly in the temporal and frontal lobes. We envisage that our data set, shared openly with the community, will catalyze the development and application of computational methods in clinical neurology.

PPB-Affinity: Protein-Protein Binding Affinity dataset for AI-based protein drug discovery

Prediction of protein-protein binding (PPB) affinity plays an important role in large-molecular drug discovery. Deep learning (DL) has been adopted to predict the changes of PPB binding affinities upon mutations, but there was a scarcity of studies predicting the PPB affinity itself. The major reason is the paucity of open-source dataset with PPB affinity data. To address this gap, the current study introduced a large comprehensive PPB affinity (PPB-Affinity) dataset. The PPB-Affinity dataset contains key information such as crystal structures of protein-protein complexes (with or without protein mutation patterns), PPB affinity, receptor protein chain, ligand protein chain, etc. To the best of our knowledge, this is the largest publicly available PPB affinity dataset, and we believe it will significantly advance drug discovery by streamlining the screening of potential large-molecule drugs. We also developed a deep-learning benchmark model with this dataset to predict the PPB affinity, providing a foundational comparison for the research community.

Development of an Open-Source Dataset of Flat-Mounted Images for the Murine Oxygen-Induced Retinopathy Model of Ischemic Retinopathy.

To describe an open-source dataset of flat-mounted retinal images and vessel segmentations from mice subject to the oxygen-induced retinopathy (OIR) model. Flat-mounted retinal images from mice killed at postnatal days 12 (P12), P17, and P25 used in prior OIR studies were compiled. Mice subjected to normoxic conditions were killed at P12, P17, and P25, and their retinas were flat-mounted for imaging. Major blood vessels from the OIR images were manually segmented by four graders (JSC, HKR, KBL, JM), with cross-validation performed to ensure similar grading. Overall, 1170 images were included in this dataset. Of these images, 111 were of normoxic mice retina, and 1048 were mice subject to OIR. The majority of images from OIR mice were obtained at P17. The 50 images obtained from an external dataset, OIRSeg, did not have age labels. All images were manually segmented and used in the training or testing of a previously published deep learning algorithm. This is the first open-source dataset of original and segmented flat-mounted retinal images. The dataset has potential applications for expanding the development of generalizable and larger-scale artificial intelligence and analyses for OIR. This dataset is published online and publicly available at dx.doi.org/10.6084/m9.figshare.23690973. This open access dataset serves as a source of raw data for future research involving big data and artificial intelligence research concerning oxygen-induced retinopathy.

HSF-IBI: A Universal Framework for Extracting Inter-Beat Interval from Heterogeneous Unobtrusive Sensors

Heartbeat inter-beat interval (IBI) extraction is a crucial technology for unobtrusive vital sign monitoring, yet its precision and robustness remain challenging. A promising approach is fusing heartbeat signals from different types of unobtrusive sensors. This paper introduces HSF-IBI, a novel and universal framework for unobtrusive IBI extraction using heterogeneous sensor fusion. Specifically, harmonic summation (HarSum) is employed for calculating the average heart rate, which in turn guides the selection of the optimal band selection (OBS), the basic sequential algorithmic scheme (BSAS)-based template group extraction, and the template matching (TM) procedure. The optimal IBIs are determined by evaluating the signal quality index (SQI) for each heartbeat. The algorithm is morphology-independent and can be adapted to different sensors. The proposed algorithm framework is evaluated on a self-collected dataset including 19 healthy participants and an open-source dataset including 34 healthy participants, both containing heterogeneous sensors. The experimental results demonstrate that (1) the proposed framework successfully integrates data from heterogeneous sensors, leading to detection rate enhancements of 6.25 % and 5.21 % on two datasets, and (2) the proposed framework achieves superior accuracy over existing IBI extraction methods, with mean absolute errors (MAEs) of 5.25 ms and 4.56 ms on two datasets.

Manifold learning-empowered lightweight unsupervised deep representation for edge-side fault detection

This paper addresses the problem of fault diagnosis in rotating systems by monitoring and analysis of their vibration signals. To tackle the prevalent challenges posed by the scarcity of high-quality labelled data and the complexity of models in the context of intelligent fault diagnosis for edge computing deployments, this research introduces a lightweight unsupervised deep representation method for edge-side fault detection. By leveraging manifold learning, this approach places a strong emphasis on unsupervised learning and model compactness. Firstly, the approach utilises the non-linear fitting capabilities of deep learning to implement manifold learning's dimensionality reduction, which can efficiently extract structured relationships from high-dimensional data, enabling rapid mapping of unlabelled samples to a lower-dimensional space. Then, the proposed method includes a clustering algorithm to refine the model's accuracy by correcting pseudo-labels near category centres, reducing intra-class distances and enhancing low-dimensional clustering. Finally, the model employs the isolation forest (iForest) algorithm for the rapid identification of both known and novel fault types at the edge, an innovative step in fault detection technology. Experiments on an open-source dataset and a real-world fault dataset collected by a customised test-rig indicate that the model proves effective in complex scenarios involving unlabelled data, sample imbalances and emerging fault categories. Its success underscores its significant potential for real-time online fault diagnosis in industrial environments, marking a substantial advancement in the field of intelligent fault identification.

A new mining and decoding framework to predict expression of opinion on social media emoji’s using machine learning models

<span lang="EN-US">This research work proposes a new framework mining and decoding (MindE) to predict the expression of opinion on social media emojis using machine learning (ML) models. Expression of opinion can be predicted with short messages on social media. This study used two groups of ML algorithms, convolutional neural network (CNN) ImageNet and CNN AlexNet classifier, and finally, applied the decision tree classifier to predict the type of expression. A recent dataset was taken from Kaggle, an open-source dataset consisting of 7476 rows of emojis for expression of opinion prediction. Accuracy was computed with a G power of 80%, and the experiment was repeated 20 times using both models. After the introduction of the proposed MindE framework, the performance of an expression of opinion prediction will be analyzed with accuracy level. The CNN ImageNet achieved an impressive 97.32% accuracy, whereas the CNN AlexNet algorithm reached only 85.98%. The independent sample T Test indicated a p-value of 0.001, which is below the significance level of 0.05. This suggests that the performance difference between the two ML algorithms is statistically significant. Consequently, the results strongly support the proposed framework “MindE” to predict the expression of opinion on social media emojis.</span>

From PIV to LSPIV: Harnessing deep learning for environmental flow velocimetry

The inference of velocity fields from the displacement of objects and/or fields visible within a series of consecutive images over known time intervals has been explored extensively within experimental fluid dynamics. Real image sequences of environmental hydrodynamic flows, however, pose additional challenges for velocity field inference due to factors such as lighting inhomogeneity, particle density, camera orientation and stability. Here we investigate the performance of classical and deep learning based velocity estimation methods on three experimental datasets; a hydrodynamics laboratory dataset of different flow types and two open-source datasets of aerial river footage from field campaigns. The river datasets are accompanied by observational datasets of in-situ measurements. In particular, we investigate the generalisation of deep learning based methods from ideal training conditions to real images. We consider three deep learning approaches; recurrent all-pairs-field transforms (RAFT), a physics-informed approach and an unsupervised learning approach (UnLiteFlowNet-PIV). Results indicate that RAFT, which achieves state-of-the-art performance on particle image datasets, showed good generalisation to the laboratory dataset and field imagery. The physics-informed approach performed similarly to RAFT across the laboratory dataset whilst generalisation to drone-based data proved challenging. Across the laboratory dataset, UnLiteFlowNet-PIV showed good performance within wake regions but an underestimation of channel flows and freestream regions with limited vorticity, also suffering under poor seeding density. Limited fine-tuning of UnLiteFlowNet-PIV on laboratory data, however, led to improved performance in these regions, indicating the potential of the unsupervised learning approach for environmental flows where 2D ground truth data sources are unavailable for training.

Advanced montane bird monitoring using self-supervised learning and transformer on passive acoustic data

Passive acoustic monitoring combined with deep learning-based bird sound classifiers is an effective tool, particularly in remote areas. While self-supervised learning has recently excelled in natural language processing and image recognition, its application to bird sound recognition remains limited. This study proposes an innovative self-supervised learning approach, which leverages vast amounts of passive acoustic recordings for pre-training, followed by fine-tuning of target species. Compared to the three state-of-the-art models based on transfer learning from ImageNet, the proposed method demonstrated improvements across all species, with even more significant gains for tail-end species. These results confirm that domain-specific pre-training in self-supervised learning enhances downstream recognition tasks and provides greater robustness, benefiting tail-end species in imbalanced ecological datasets. Our experiments further demonstrate that integrating open-source datasets and data augmentation techniques is the most effective strategy for mitigating data imbalances and cross-domain issues. In addition, introducing a ‘catch-all’ category into training datasets has been shown to improve model robustness in open set recognition scenarios. We also identified the minimum viable sample size requirements for our proposed model and explored the impact of overlapping bird vocalizations during dawn choruses on model performance. Targeting 31 bird species in the montane regions of subtropical Taiwan, the model achieved a class-wise mean average precision of 0.782 and an overall precision of 85.6 % at the F0.5 threshold in dawn chorus soundscape recordings. This study confirms the effectiveness and advantages of self-supervised learning in bird sound recognition, supporting long-term monitoring of bird distribution and vocal activity in remote montane areas.

Data race detection via few-shot parameter-efficient fine-tuning

The OpenMP programming model is playing an increasing role in parallelization on shared-memory systems owing to its simplicity of operation and portability. OpenMP provides the semantic equivalent of a parallel program for the original sequential program. Though it is easier to write parallel programs using OpenMP, writing them correctly is a challenge. Data race conditions errors can easily occur during the writing process, particularly by inexperienced programmers. Some data race checkers have been developed to help programmers check for data race in parallel programs. However, several of them have constraints on the input and thread configuration, time overhead, and scope of program analysis. In this study, we target data race detection in OpenMP parallel programs to address the issues of constraints from checkers. We propose a few-shot parameter-efficient fine-tuning method using adapter module to address data race detection issue. The proposed method does not require a large labeled dataset, and it makes data efficient. A generic dataset is constructed with a limited number of labeled data, containing diverse OpenMP patterns for data race detection. A neural architecture search approach is employed to improve the performance of detection. The experimental results on the generated and open-source datasets demonstrate that our method is effective and improves race detection compared with traditional methods.

Deep Semantics-Enhanced Neural Code Search

Code search uses natural language queries to retrieve code snippets from a vast database, identifying those that are semantically similar to the query. This enables developers to reuse code and enhance software development efficiency. Most existing code search algorithms focus on capturing semantic and structural features by learning from both text and code graph structures. However, these algorithms often struggle to capture deeper semantic and structural features within these sources, leading to lower accuracy in code search results. To address this issue, this paper proposes a novel semantics-enhanced neural code search algorithm called SENCS, which employs graph serialization and a two-stage attention mechanism. First, the code program dependency graph is transformed into a unique serialized encoding, and a bidirectional long short-term memory (LSTM) model is used to learn the structural information of the code in the graph sequence to generate code vectors rich in structural features. Second, a two-stage attention mechanism enhances the embedded vectors by assigning different weight information to various code features during the code feature fusion phase, capturing significant feature information from different code feature sequences, resulting in code vectors rich in semantic and structural information. To validate the performance of the proposed code search algorithm, extensive experiments were conducted on two widely used code search datasets, CodeSearchNet and JavaNet. The experimental results show that the proposed SENCS algorithm improves the average code search accuracy metrics by 8.30 % (MRR) and 17.85% (DCG) and compared to the best baseline code search model in the literature, with an average improvement of 14.86% in the SR@1 metric. Experiments with two open-source datasets demonstrate SENCS achieves a better search effect than state of-the-art models.

Efficient data processing pipeline for event-based vision datasets: techniques and insights

Abstract Event-based vision datasets have emerged as a critical asset in advancing the capabilities of real-time perception systems, particularly in fields such as robotics, autonomous vehicles, and human-computer interaction. These datasets enable low-latency processing by capturing asynchronous, pixel-level changes in the scene, providing a distinct advantage over traditional frame-based systems. However, the diverse formats and characteristics of event-based datasets pose significant challenges for efficient processing and analysis, hindering their broader adoption and integration. In this paper, we present a versatile and comprehensive data processing pipeline designed to address these challenges by supporting multiple event-data formats, including newer formats such as EVT2 and EVT3. This pipeline not only converts data into widely supported formats like AEDAT and NPZ, but also ensures that the unique characteristics of event-based data-such as temporal precision and sparse event representation-are preserved throughout the conversion process. By applying this pipeline to several open-source datasets, we establish a standardized, efficient methodology for dataset manipulation that enhances compatibility and reproducibility in event-based vision research. Additionally, we introduce a novel high-resolution event-based action dataset, converted into various formats using our pipeline, which opens new avenues for exploring event-based techniques in action recognition. This dataset and our pipeline serve as valuable resources for the research community, enabling advancements in real-time vision applications and fostering greater collaboration and standardization across studies.

Deep Learning-Based Student Engagement Classification in Online Learning

Online education has gained significant popularity during the COVID-19 pandemic. The availability of massive open online courses has further strengthened the online learning environment. One of the considerable challenges in online learning environments is to measure the learner’s engagement to meet the educational objectives. This paper addresses the challenge and proposes a convolutional neural network-based architecture that extracts discriminative features from the face (Affective) and the upper body (Behavioral) of the learner and classifies the engagement. The performance of the proposed architecture is evaluated on the publicly available Dataset of Affective States In E-Environments (DAiSEE) and the learning-centered affective state dataset curated from open-source datasets. The experimental results demonstrate that the proposed methodology with affective and behavioral features improves the engagement measurement results. The proposed method outperforms the state-of-the-art in terms of Unweighted Average Recall (UAR), Unweighted Average Precision (UAP), and Unweighted Average F1 (UAF1) with 79.14%, 49.62%, and 55.29% values, respectively. It is threefold more computationally efficient than the previous state-of-the-art method. The proposed method also improves the accuracy of the curated dataset by 2.07% of the prior method.

MOVRO2: Loosely coupled monocular visual radar odometry using factor graph optimization

Ego-motion estimation is an indispensable part of any autonomous system, especially in scenarios where wheel odometry or global pose measurement is unreliable or unavailable. In an environment where a global navigation satellite system is not available, conventional solutions for ego-motion estimation rely on the fusion of a LiDAR, a monocular camera and an inertial measurement unit (IMU), which is often plagued by drift. Therefore, complementary sensor solutions are being explored instead of relying on expensive and powerful IMUs. In this paper, we propose a method for estimating ego-motion, which we call MOVRO2, that utilizes the complementarity of radar and camera data. It is based on a loosely coupled monocular visual radar odometry approach within a factor graph optimization framework. The adoption of a loosely coupled approach is motivated by its scalability and the possibility to develop sensor models independently. To estimate the motion within the proposed framework, we fuse ego-velocity of the radar and scan-to-scan matches with the rotation obtained from consecutive camera frames and the unscaled velocity of the monocular odometry. We evaluate the performance of the proposed method on two open-source datasets and compare it to various mono-, dual- and three-sensor solutions, where our cost-effective method demonstrates performance comparable to state-of-the-art visual-inertial radar and LiDAR odometry solutions using high-performance 64-line LiDARs.

Generative modeling of the Circle of Willis using 3D-StyleGAN

The circle of Willis (CoW) is a network of cerebral arteries with significant inter-individual anatomical variations. Deep learning has been used to characterize and quantify the status of the CoW in various applications for the diagnosis and treatment of cerebrovascular disease. In medical imaging, the performance of deep learning models is limited by the diversity and size of training datasets. To address medical data scarcity, generative AI models have been applied to generate synthetic vessel neuroimaging data. However, the proposed methods produce synthetic data with limited anatomical fidelity or downstream utility in tasks concerning vessel characteristics.We adapted the StyleGANv2 architecture to 3D to synthesize Time-of-Flight Magnetic Resonance Angiography (TOF MRA) volumes of the CoW. For generative modeling, we used 1782 individual TOF MRA scans from 6 open source datasets. To train the adapted 3D StyleGAN model with limited data we employed differentiable data augmentations, used mixed precision and a cropped region of interest of size 32 × 128 × 128 to tackle computational constraints. The performance was evaluated quantitatively using the Fréchet Inception Distance (FID), MedicalNet distance (MD) and Area Under the Curve of the Precision and Recall Curve for Distributions (AUC-PRD). Qualitative analysis was performed via a visual Turing test. We demonstrated the utility of generated data in a downstream task of multiclass semantic segmentation of CoW arteries. Vessel segmentation performance was assessed quantitatively using the Dice coefficient and the Hausdorff distance.The best-performing 3D StyleGANv2 architecture generated high-quality and diverse synthetic TOF MRA volumes (FID: 12.17, MD: 0.00078, AUC-PRD: 0.9610). Multiclass vessel segmentation models trained on synthetic data alone achieved comparable performance to models trained using real data in most arteries. The addition of synthetic data to a baseline training set improved segmentation performance in underrepresented artery segments, similar to the addition of real data.In conclusion, generative modeling of the Circle of Willis via synthesis of 3D TOF MRA data paves the way for generalizable deep learning applications in cerebrovascular disease. In the future, the extensions of the provided methodology to other medical imaging problems or modalities with the inclusion of pathological datasets has the potential to advance the development of more robust AI models for clinical applications.

Medical language model specialized in extracting cardiac knowledge

The advent of the Transformer has significantly altered the course of research in Natural Language Processing (NLP) within the domain of deep learning, making Transformer-based studies the mainstream in subsequent NLP research. There has also been considerable advancement in domain-specific NLP research, including the development of specialized language models for medical. These medical-specific language models were trained on medical data and demonstrated high performance. While these studies have treated the medical field as a single domain, in reality, medical is divided into multiple departments, each requiring a high level of expertise and treated as a unique domain. Recognizing this, our research focuses on constructing a model specialized for cardiology within the medical sector. Our study encompasses the creation of open-source datasets, training, and model evaluation in this nuanced domain.

A new outlier rejection approach for non-Lambertian photometric stereo

Photometric stereo (PS) has garnered increasing attention due to its adeptness in restoring local fine textures. However, non-Lambertian reflections present in almost all real-world objects limit the effectiveness of the Lambertian model for surface normal vector estimation. Although BRDF-based and deep learning-based methods have become mainstream, recent research shows that comparable accuracy can also be achieved through simple filtering of observed intensity values. Nevertheless, these methods only consider the relative bias of pixel values and require manual specification of the number of pixels to be culled and the number of iterations. To address these issues, this paper proposes corresponding improvement methods. Firstly, a weighted inter-relationship function (IRF) fused with Huber loss is introduced to robustly and effectively evaluate the abnormal degree of pixel value. Secondly, based on the IRF curve and histogram statistical analysis, the number of excluded pixels is adaptively calculated. Thirdly, linear equations are then constructed based on the photometric equations, and the maximum between-class variance method is employed to achieve a high degree of sparsity, enabling fast and effective normal vector estimation. Finally, to verify the effectiveness of the proposed algorithm for non-Lambertian PS vision, quantitative verification tests on the synthetic dataset and the open-source datasets “DiLiGenT” and “DiLiGenT-PI” in real-world scenarios, and qualitative assessment experiments on real metal roughness samples are conducted. The experimental results demonstrate that, compared with the position threshold and IRF methods, our algorithms not only significantly enhance the accuracy of normal vector solutions but also markedly improve the operational efficiency of the algorithm, laying a solid foundation for practical online applications. These results fully validate the correctness and effectiveness of the proposed algorithm and provide a reference for the further development of outlier removal algorithms.

Visualizing the association between the location and prognosis of isocitrate dehydrogenase wild-type glioblastoma: a voxel-wise Cox regression analysis with open-source datasets.

This study examined the correlation between tumor location and prognosis in patients with glioblastoma using magnetic resonance images of various isocitrate dehydrogenase (IDH) wild-type glioblastomas from The Cancer Imaging Archive (TCIA). The relationship between tumor location and prognosis was visualized using voxel-wise Cox regression analysis. Participants with IDH wild-type glioblastoma were selected, and their survival and demographic data and tumor characteristics were collected from TCIA datasets. Post-contrast-enhanced T1-weighted imaging, T2-fluid attenuated inversion recovery imaging, and tumor segmentation data were also compiled. Following affine registration of each image and tumor segmentation region of interest to the MNI standard space, a voxel-wise Cox regression analysis was conducted. This analysis determined the association of the presence or absence of the tumor with the prognosis in each voxel after adjusting for the covariates. The study included 769 participants of 464 men and 305 women (mean age, 63years ± 12 [standard deviation]). The hazard ratio map indicated that tumors in the medial frontobasal region and around the third and fourth ventricles were associated with poorer prognoses, underscoring the challenges of complete resection and treatment accessibility in these areas regardless of the tumor volume. Conversely, tumors located in the right temporal and occipital lobes had favorable prognoses. This study showed an association between tumor location and prognosis. These findings may assist clinicians in developing more precise and effective treatment plans for patients with glioblastoma to improve their management.