Traditional Image Research Articles

Research on identification and detection of coal and gangue based on infrared thermal imaging technology and improved YOLOv8n

ABSTRACT Considering that traditional image recognition methods for coal and gangue are susceptible to environmental factors such as light and dust, this paper proposes an image recognition and detection method for the coal and gangue based on infrared thermal imaging technology and an improved YOLOv8n. First, we established a thermal infrared imaging platform for coal and gangue. We used the YOLOv8n model to analyze the differences between coal and gangue at various temperatures, determining the optimal temperature for effective recognition of the coal and gangue. Next, to facilitate the practical deployment of the detection model for coal and gangue, we incorporated the Ghost module into the original YOLOv8n model to reduce its complexity and storage requirements. Unfortunately, incorporating the Ghost module led to a slight decrease in the model’s detection performance. Next, we introduced the DAttention module into the model to enhance its feature extraction capabilities. At the same time, we replaced the CIoU loss function with DIoU, which further improved the model’s performance metrics. Finally, we compared the improved YOLOv8n model with Faster-RCNN model and SSD model to validate its feasibility and accuracy further and provide a new approach for the detection of coal and gangue.

Deep Learning Models for Early Detection of Ovarian Cancer: A Systematic Review of Ultrasound-Based Diagnostic Tools

Ovarian Cancer (OC) remains one of the most lethal gynecological malignancies, with a global 5-year survival rate of only 45%. This project aimed to assess the potential of deep learning-based diagnostic tools in improving early detection of ovarian cancer, particularly through ultrasound imaging. The primary research problem lies in the challenge of accurately diagnosing OC at an early stage, as traditional imaging techniques often rely on subjective interpretations, leading to inconsistent results. To address this issue, a systematic review was conducted following PRISMA guidelines, evaluating studies published between January 2018 and May 2023 in the Scopus and PubMed databases. These studies employed deep learning or artificial intelligence models for the diagnosis, prognosis, or identification of OC from ultrasound images. Of the 101 studies screened, 9 met the inclusion criteria. The included studies reported diagnostic accuracies of deep learning models ranging from 75% to 100%, with sensitivities between 85% and 99%. The conclusions indicate that deep learning models significantly enhance the diagnostic accuracy of ovarian cancer, offering a promising non-invasive tool for early detection. This research underscores the importance of integrating AI technologies into clinical practice to improve survival outcomes for OC patients.

Contrast-Enhanced Micro-CT Imaging of Murine Mandibles: A Multi-Method Approach for Simultaneous Hard and Soft Tissue Analysis.

To develop and evaluate a novel multi-method micro-computed tomography (μCT) imaging protocol for enhanced visualization of both hard and soft tissues in murine mandibles, addressing the limitations of traditional imaging techniques in dental research. We employed a contrast-enhanced (CE) μCT imaging technique using Lugol's iodine as a contrast agent to visualize the intricate structures of murine mandibles. The protocol involved the combination of conventional μCT imaging as well as CE-μCT, including decalcification with EDTA, allowing for simultaneous assessment of hard and soft tissues. The method is compared with standard imaging modalities, and the ability to visualize detailed anatomical features is discussed. The CE-μCT imaging technique provided superior visualization of murine mandibular structures, including dental pulp, periodontal ligaments and the surrounding soft tissues, along with conventional μCT imaging of alveolar bone and teeth. This method revealed detailed anatomical features with high specificity and contrast, surpassing traditional imaging approaches. Our findings demonstrate the potential of CE-μCT imaging with Lugol's iodine as a powerful tool for dental research. This technique offers a comprehensive view of the murine mandible, facilitating advanced studies in tissue engineering, dental pathology and the development of dental materials.

The Role of Nuclear Medicine in the Diagnostic Work-Up of Athletes: An Essential Guide for the Sports Cardiologist.

Athletes with heart disease are at increased risk of malignant ventricular arrhythmias and sudden cardiac death compared to their sedentary counterparts. When athletes have symptoms or abnormal findings at preparticipation screenings, a precise diagnosis by differentiating physiological features of the athlete's heart from pathological signs of cardiac disease is as important as it is challenging. While traditional imaging methods such as echocardiography, cardiac magnetic resonance, and computed tomography are commonly employed, nuclear medicine offers unique advantages, especially in scenarios requiring stress-based functional evaluation. This article reviews the use of nuclear medicine techniques in the diagnostic work-up of athletes with suspected cardiac diseases by highlighting their ability to investigate myocardial perfusion, metabolism, and innervation. The article discusses the application of single photon emission computed tomography (SPECT) and positron emission tomography (PET) using radiotracers such as [99mTc]MIBI, [99mTc]HDP, [18F]FDG, and [123I]MIBG. Several clinical scenarios are explored, including athletes with coronary atherosclerosis, congenital coronary anomalies, ventricular arrhythmias, and non-ischemic myocardial scars. Radiation concerns are addressed, highlighting that modern SPECT and PET equipment significantly reduces radiation doses, making these techniques safer for young athletes. We conclude that, despite being underutilized, nuclear medicine provides unique opportunities for accurate diagnosis and effective management of cardiac diseases in athletes.

A novel image inpainting method based on a modified Lengyel–Epstein model

With the increasing popularity of digital images, developing advanced algorithms that can accurately reconstruct damaged images while maintaining high visual quality is crucial. Traditional image restoration algorithms often struggle with complex structures and details, while recent deep learning methods, though effective, face significant challenges related to high data dependency and computational costs. To resolve these challenges, we propose a novel image inpainting model, which is based on a modified Lengyel–Epstein (LE) model. We discretize the modified LE model by using an explicit Euler algorithm. A series of restoration experiments are conducted on various image types, including binary images, grayscale images, index images, and color images. The experimental results demonstrate the effectiveness and robustness of the method, and even under complex conditions of noise interference and local damage, the proposed method can exhibit excellent repair performance. To quantify the fidelity of these restored images, we use the peak signal-to-noise ratio (PSNR), a widely accepted metric in image processing. The calculation results further demonstrate the applicability of our model across different image types. Moreover, by evaluating CPU time, our method can achieve ideal repair results within a remarkably brief duration. The proposed method validates significant potential for real-world applications in diverse domains of image restoration and enhancement.

Deep learning assisted characterization of bubble behavior in a gas-solid fluidized bed with binary particle mixtures

Gas-solid fluidized beds are widely applied as chemical reactors, and the fluidization quality in the bed is closely related to bubble behavior. Digital image processing is a commonly used non-invasive method for bubble behavior analysis, but it is usually constrained by experimental conditions such as lighting, making identification of bubble and emulsion phases still challenging. Herein, deep learning is applied in this study to optimize traditional digital image processing techniques. By evaluating different deep learning models (FCN, DeepLab V3, U-Net), rapid and accurate identification and segmentation of bubble images can be achieved, and the U-Net model performs best, achieving an identification accuracy of 99.05 %. Further application of U-Net to analyze bubble behavior demonstrates that deep learning methods enable efficient and accurate identification of bubbles and real-time analysis of bubble behavior, highlighting the significant potential application of deep learning in the field of complex hydrodynamics in fluidized beds.

Simultaneous In Vivo Imaging of Neutrophil Elastase and Oxidative Stress in Atherosclerotic Plaques Using a Unimolecular Photoacoustic Probe.

Atherosclerosis, a major global health concern with high morbidity and mortality rates, involves complex interactions of chronic inflammation, oxidative stress, and proteolytic enzymes. Conventional imaging methods struggle to capture the dynamic biochemical processes in atherosclerotic plaques. Here, we introduce a novel unimolecular photoacoustic probe (UMAPP) designed with specific binding sites for neutrophil elastase (NE) and the redox pair O2⋅-/GSH, enabling real-time monitoring of oxidative stress and activated neutrophils in plaques. UMAPP, comprising a boron-dipyrromethene (BODIPY) core linked to a hydrophilic NE-cleavable tetrapeptide and dual oxidative stress-responsive catechol moieties, facilitates NE-mediated modulation of photoinduced electron transfer impacting photoacoustic intensity at 685 nm (PA685). Furthermore, oxidation and reduction of catechol groups by O2⋅- and GSH induce reversible, ratiometric changes in the photoacoustic spectrum (PA745/PA685 ratio). Initial UMAPP applications successfully distinguished atherosclerotic and healthy mice, evaluated pneumonia's effect on plaque composition and verified the probe's effectiveness in drug-treatment studies by detecting molecular alterations before visible histopathological changes. The integrated molecular imaging capabilities of UMAPP offer promising advancements in atherosclerosis diagnosis and management, enabling early and accurate identification of vulnerable plaques.

Front and Back Covers, Volume 40, Number 5. October 2024

Front and back cover caption, volume 40 issue 5ALGORITHMIC POLICING & AIThis image captures Roberto J. González's exploration of algorithmic policing in America in this issue. Cameras survey a stylized urban landscape, symbolizing the pervasive reach of artificial intelligence in modern law enforcement.González examines various technologies of surveillance: predictive policing platforms, facial recognition software and acoustic gunshot detection systems. Its algorithmic composition hints at the complex mathematical models underpinning these tools, which claim to forecast crime and identify potential offenders.Below, the cityscape appears as a mix of physical structures and data points, blurring the line between the tangible world and its digital representation. This visual metaphor represents the technologies that are reshaping our understanding of urban spaces, often reinforcing existing patterns of surveillance and control in marginalized communities.The ominous tone and starkness of the image echo the article's critical perspective on the unchecked expansion of algorithmic policing. The article raises questions about privacy, consent, and the potential for these systems to amplify biases and errors in law enforcement decisions.The human stories behind these technologies – from wrongful arrests to community distrust – are often obscured by promises of efficiency and safety. This reminds us of the need for anthropological scrutiny of these roboprocesses and their impact on social fabrics.The unseen digital infrastructure is increasingly shaping policing practices, reshaping the landscape of public safety and surveillance in America and beyond. This raises important questions about how venture capital, tech startups, and law enforcement agencies are transforming traditional notions of policing and civil liberties in the digital age.Back cover caption, volume 40 issue 5HANDS OF CHANGE: FARMER IDENTITYThis image of the weathered hands of a farmer, cradling rich soil, encapsulates the complex reality of modern European agriculture explored in Eimear Mc Loughlin's article. These hands, which have long symbolized the deep connection between farmers and the land they cultivate, now tell a more nuanced story of an identity in flux.According to Mc Loughlin, the traditional image of the farmer is vanishing because of new pressures that are becoming more intense. These hands, once primarily engaged in the tactile work of cultivating crops and tending to animals bred for food, now increasingly grapple with spreadsheets, regulatory paperwork and the invisible constraints of global markets. The soil they hold represents not just the promise of growth, but also the weight of environmental concerns, stringent regulations and societal expectations.Yet, this image also speaks to the enduring essence of farming that persists despite these challenges. It reminds us of the irreplaceable role farmers play in our food systems and national identities, even as their traditional image evolves. The rich soil suggests the potential for renewal and adaptation, mirroring Mc Loughlin's call for a reimagined agricultural future.As Europe's farmers protest and struggle with their changing identity, this image reminds us of what is at stake. It challenges us to consider how we can support a transition to sustainable farming that reflects the hands‐on work symbolized here while addressing pressing environmental and economic realities. In doing so, we might forge a path where the figure of the farmer does not disappear but evolves, maintaining that crucial connection to the land while navigating the complexities of modern agriculture.

Few-shot learning based histopathological image classification of colorectal cancer

Background Colorectal cancer is a prevalent and deadly disease worldwide, posing significant diagnostic challenges. Traditional histopathologic images classification is often inefficient and subjective. Although some histopathologists use computer-aided diagnosis (CAD) to improve efficiency, these methods depend heavily on extensive data and specific annotations, limiting their development. To address these challenges, this paper proposes a method based on few-shot learning. Methods This study introduces a few-shot learning approach that combines transfer learning and contrastive learning to classify colorectal cancer histopathology images into benign and malignant categories. The model comprises modules for feature extraction, dimensionality reduction, and classification, trained using a combination of contrast loss and cross-entropy loss. In this paper, we detail the setup of hyperparameters:n-way, k-shot, β and the creation of support, query, and test datasets. Results Our method achieved over 98% accuracy on a query dataset with 35 samples per category using only 10 training samples per category. We documented the model’s loss, accuracy, and the confusion matrix of the results. Additionally, we employed the t-SNE algorithm to analyze and assess the model’s classification performance. Conclusion The proposed model demonstrates significant advantages in accuracy and minimal data dependency, performing robustly across all tested n-way, k-shot scenarios. It consistently achieved over 93% accuracy on comprehensive test datasets, including 1916 samples, confirming its high classification accuracy and strong generalization capability. Our research advances the use of few-shot learning in medical diagnostics, and also lays the groundwork for extending it to deal with rare, difficult-to-diagnose cases.

Contour and texture preservation underwater image restoration via low-rank regularizations

Due to the wavelength-dependent absorption and scattering, underwater images are often degraded by color distortion, haze, and low contrast. Despite the great advancements achieved in improving the quality of underwater image, the loss of contour and texture details in the restored results remains a major obstacle for subsequent high-level visual tasks. To this end, we develop a novel variational model, including one data term and two fidelity terms, for underwater image restoration. Technically, according to Retinex theory, we first extend the traditional underwater image formation (UIF) model by decomposing the total radiance into the reflectance component and illumination component. Based on the extended UIF model, two low-rank penalty regularization terms are designed to constrain the gradients of reflectance and illumination. Furthermore, we innovatively incorporate the weighted fusion bright-dark channel algorithm and the fusion upper-middle channel prior algorithm into the proposed variational model for estimating the local background light (ambient illumination) and transmission map, respectively. Simultaneously, based on the alternating direction multiplier method (ADMM), we develop a fast optimization algorithm to speed up the iterative process. Extensive experiments demonstrate that the proposed method is effective in removing haze, enhancing contrast and sharpness, as well as preserving the image contour and texture details. Comprehensive comparisons on two large-scale underwater image datasets further validate the superiority of our method against several state-of-the-art methods in both qualitative and quantitative evaluations. The code of the proposed method will be available soon.

Crustal imaging and characterization of active faults with a large-N nodal deployment - Application to the Chaînons Béarnais region (western Pyrenees foothills, France)

Understanding the driving forces and nature of intraplate seismicity remains a major unsolved problem in seismology. In the western Pyrénées, seismicity is concentrated in a narrow region that follows the boundary between the Axial Zone and the North Pyrenean Zone. Despite the presence of a permanent network in the region, the geometry of active faults, and their relationship with crustal structures, remain elusive, owing to significant earthquake location uncertainties. Here, we exploit data recorded by a large-N nodal array deployed in the Chaînons Béarnais region during four weeks of 2022 in order to image crustal structures and characterize active faults. We automatically detected and picked P and S waves with PhaseNet, resulting in a catalog of over 500 events, half of which are located beneath the temporary deployment. Tomographic images obtained from the inversion of P and S arrival times provide detailed insight into the geometry of folds and thrusts in the sedimentary cover, as well as the presence of a main fault in the basement which dips northward with an angle of 65° (Chaînons Béarnais normal fault). Seismicity relocation within the 3D model obtained by tomography shows that earthquakes are concentrated along this main active fault, extending from the top of the basement to a depth of approximately 16 km. These results demonstrate that passive imaging approaches can offer cost-effective alternatives to traditional controlled source imaging for seismotectonic studies and natural resource exploration in regions with active seismicity.

Enhancing dynamic target reconstruction and tracking based on ghost imaging and deep convolutional neural networks

Ghost imaging requires a large amount of sampling data, which limits its applications in the study of dynamic objects. Here, we propose an imaging technique based on deep convolutional neural networks (SaDunet) that can be used to examine the dynamics of target objects. By replacing the traditional correlation imaging reconstruction approach with SaDunet, the ability to recover high-quality images at low sampling rates is enhanced. The motion process of the target object is decomposed into multiple motion frames, and then each frame is imaged separately. Experiments show that the reconstructed image of the target object obtained by this scheme is of high quality, contains almost no noise, and accurately reflects the motion behavior of the target object.

Non-Destructive Identification of Virgin Cashmere and Chemically Modified Wool Fibers Based on Fractional Order Derivative and Improved Wavelength Extraction Algorithm Using NIR Spectroscopy and Chemometrics

ABSTRACT Virgin cashmere is highly prized for its superior quality, and chemically modified wool is often used to fake it. However, traditional spectral analysis and image processing methods struggle to identify the two fibers. To address this, combining chemometrics with near-infrared (NIR) spectroscopy is proposed to identify virgin cashmere and chemically modified wool based on fractional order derivatives and improved wavelength extraction algorithm. The original spectra of the two fibers are very close, making identification difficult. Therefore, fractional order derivatives are used to amplify tiny spectral differences, which are often overlooked in conventional analysis. Meanwhile, common wavelength extraction algorithms few consider inherent relationship between spectral features and chemical properties, so an improved wavelength extraction algorithm using Shuffled Frog Leaping Algorithm (SFLA) and Beluga Whale Optimization (BWO) is employed to explore the connection of the two. The proposed method with Partial Least Squares Discriminant Analysis (PLS-DA) achieves an accuracy of 94.4%. Experimental results demonstrate that fractional order derivatives enhance tiny spectral features and differences, while extracted feature wavelengths correspond to main chemical characteristic bands of the fibers. This study shows the feasibility of identifying virgin cashmere and chemically modified wool and offers a novel idea for identifying fibers in the textile industry.

Digital Protection and Inheritance Path of Intangible Cultural Heritage based on Image Processing Algorithm

This research paper introduces a novel approach in the realm of digital preservation and inheritance of Intangible Cultural Heritage (ICH) through a Customized 3D Convolutional Neural Network (CNN). The core of this study lies in the development of an advanced image processing algorithm tailored to accurately recognize, categorize, and archive diverse forms of ICH, which include traditional performances, ceremonies, oral traditions, and crafts. Utilizing a volumetric 3D CNN, this paper demonstrates how complex ICH elements can be effectively captured and analyzed, overcoming the limitations of traditional 2D image processing methods. The network is trained on a comprehensive dataset of ICH imagery, ensuring sensitivity to the subtle nuances and dynamic nature of these cultural expressions. This paper highlights the algorithm’s capability in not only safeguarding the visual aspects of ICH but also in providing an interactive, digital medium for education and cultural dissemination. The proposed method shows significant promise in aiding the efforts of cultural preservationists and educators, offering a technologically advanced pathway for the protection and inheritance of the world’s rich, yet vulnerable, cultural heritage. This study sets a precedent in the interdisciplinary field of cultural heritage conservation, digital technology, and artificial intelligence, providing a scalable and effective solution for global ICH preservation initiatives.

Leukocyte detection based on convolutional neural network fusion with color Fourier ptychographic microscopy

Abstract In the field of optical microscopic imaging, color Fourier Ptychographic Microscopy (FPM) technology has attracted much attention due to its advantages of large field of view, high resolution, and quantitative phase imaging. In this paper, a color FPM fusion algorithm based on deep learning is proposed in combination with Convolutional Neural Networks (CNN) and applied to leukocyte detection. Firstly, this paper introduces a fusion model of a convolutional neural network based on the traditional color FPM imaging method and fuses low-resolution color images and high-resolution grayscale images through a multilayer convolutional network. This method improves the quality of reconstructed images while reducing the reconstruction time. Secondly, this paper constructs a leukocyte detection dataset by using an improved color FPM reconstruction algorithm and builds a leukocyte detection system based on the YOLOv7 architecture. This paper shows that combining convolutional neural networks with color FPM technology can provide higher-quality reconstructed images in medical imaging and cell detection, which provides strong technical support for digital pathology and medical diagnosis.

Design of Intelligent Algorithm for Object Search Based on IoT Digital Images

With the development of artificial intelligence, traditional object search and image recognition have been replaced by the Internet of Things and artificial intelligence. However, traditional object search algorithms often lack accuracy and low precision. Therefore, this study proposes a new intelligent encryption algorithm to address the issues of insufficient accuracy in object search algorithms and image recognition algorithms. The new algorithm ensures the security of user data and the response efficiency of the model during the conversation process by integrating fully homomorphic encryption technology and dynamic sparse attention mechanism. The dynamic sparse attention mechanism introduced simultaneously improves the model's ability to handle long sequence data by dynamically adjusting attention weights. Experimental results showed that the precision of the proposed algorithm was 0.05% higher than that of random algorithms and 0.19% higher than that of sorting algorithms. The recall rate of the proposed algorithm was 0.14% higher than that of random algorithms and 0.16% higher than that of sorting algorithms. The research algorithm can identify objects with certain characteristics and is suitable for specific environments, greatly reducing the probability of data leakage in object search and providing new ideas for research in this field.

Perceptual metric for face image quality with pixel-level interpretability

This paper tackles the shortcomings of image evaluation metrics in evaluating facial image quality. Conventional metrics do neither accurately reflect the unique attributes of facial images nor correspond with human visual perception. To address these issues, we introduce a novel metric designed specifically for faces, utilizing a learning-based adversarial framework. This framework comprises a generator for simulating face restoration and a discriminator for quality evaluation. Drawing inspiration from facial neuroscience studies, our metric emphasizes the importance of primary facial features, acknowledging that minor changes in the eyes, nose, and mouth can significantly impact perception. Another key limitation of existing image evaluation metrics is their focus on numerical values at the image level, without providing insight into how different areas of the image contribute to the overall assessment. Our proposed metric offers interpretability regarding how each region of the image is evaluated. Comprehensive experimental results confirm that our face-specific metric surpasses traditional general image quality assessment metrics for facial images, including both full-reference and no-reference methods. The code and models are available at https://github.com/AIM-SKKU/IFQA.

Fully convolutional networks-based particle distribution analysis at multiphase interfaces

The intricate interplay between droplet dynamics and particle characteristics has led to the extensive use of particle-laden droplets in diverse applications, spanning industrial engineering and scientific research. Accurate particle distribution analysis at multiphase interfaces is crucial for understanding the complex behaviors of these soft matters, for example, liquid marbles (LMs). However, traditional image analysis methods for particle-laden interfaces often rely on manual processing, which is both time-consuming and susceptible to human error. In this study, we present a pixel-level image recognition technique based on fully convolutional networks (FCNs) to automatically analyze the interfacial particle distribution of LMs exhibiting clear core-shell structures. The FCNs-based method enables efficient and accurate segmentation of target particles on sessile LMs, utilizing experimental data obtained from an in-situ optical imaging device. This approach significantly expedites the analysis of particle distribution, including proportion, concentration, and cluster centers. We assess the performance of the proposed method using image data of monolayer LMs encapsulated by dispersed polystyrene microspheres, demonstrating its superiority over traditional techniques in terms of accuracy and generalization. This investigation aims to offer novel insights into determining pertinent particulate characteristics within similar solid-liquid hybrid microsystems.

Deep Learning Based Method for Dynamic Tracking of Bubbles in Microfluidic Gas-Driven Water Experiments

Using microfluidic chips for gas displacement experiments is an important approach for studying CO2 displacement mechanisms and gas-liquid exchange processes. Analyzing the micro-scale distribution of gas and water during gas displacement enables the optimization of injection strategies, offering valuable guidance for enhancing oil and gas recovery rates. Traditional image processing techniques often face challenges in effectively handle the complex gas-liquid flow dynamics and irregular bubble shapes encountered in gas displacement experiments. To address this challenge, this paper proposes a gas bubble detection and tracking method for gas-driven water microfluidic experiments based on domain adaptation in deep learning and improved YOLOv8. Using the CycleGAN style transfer network to generate numerous simulated bubble training samples significantly reduces the manual labeling workload for bubbles. Additionally, the SE attention mechanism is integrated into the YOLOv8 backbone network to enhance the feature extraction capability of bubbles, and the Fast-PConv Head structure is incorporated into the detection head to enhance detection efficiency. Experimental results demonstrate that using just 20 annotated experimental images, the bubble detection accuracy of the proposed method can reach 94%. Lastly, DeepSORT is employed to track bubbles and visualize the dynamic trends of bubble changes in the form of visual charts. The deep learning approach proposed in this paper provides a new perspective for intelligent analysis of bubbles in microfluidic chip experiments, and future work aiming to its application to the complex multiphase flow field in porous media.

Current and Emerging Radiotracers in Molecular Cardiovascular Imaging.

Cardiovascular imaging has rapidly advanced over the past decades. Traditional imaging techniques such as echocardiography, computed tomography, and cardiovascular magnetic resonance are essential for assessing the structural and functional aspects of the cardiovascular system but often fall short in providing direct insights into disease activity. This gap is increasingly being bridged by molecular nuclear imaging techniques, including positron emission tomography and single-photon emission computed tomography, which enable the visualization of disease processes at the molecular and cellular levels. This review highlights the role of cardiovascular molecular imaging, emphasizing its current and potential applications in diagnosing and managing cardiovascular disease. With advancements in positron emission tomography scanners, novel radiotracers, and sophisticated imaging software, molecular imaging is set to play an essential role in precision medicine by enhancing our understanding of disease mechanisms, accelerating the development of targeted therapies, and facilitating personalized patient care.