Medical Image Diagnosis Research Articles

CTMLP: Can MLPs replace CNNs or transformers for COVID-19 diagnosis?

BackgroundConvolutional Neural Networks (CNNs) and the hybrid models of CNNs and Vision Transformers (VITs) are the recent mainstream methods for COVID-19 medical image diagnosis. However, pure CNNs lack global modeling ability, and the hybrid models of CNNs and VITs have problems such as large parameters and computational complexity. These models are difficult to be used effectively for medical diagnosis in just-in-time applications. MethodsTherefore, a lightweight medical diagnosis network CTMLP based on convolutions and multi-layer perceptrons (MLPs) is proposed for the diagnosis of COVID-19. The previous self-supervised algorithms are based on CNNs and VITs, and the effectiveness of such algorithms for MLPs is not yet known. At the same time, due to the lack of ImageNet-scale datasets in the medical image domain for model pre-training. So, a pre-training scheme TL-DeCo based on transfer learning and self-supervised learning was constructed. In addition, TL-DeCo is too tedious and resource-consuming to build a new model each time. Therefore, a guided self-supervised pre-training scheme was constructed for the new lightweight model pre-training. ResultsThe proposed CTMLP achieves an accuracy of 97.51%, an f1-score of 97.43%, and a recall of 98.91% without pre-training, even with only 48% of the number of ResNet50 parameters. Furthermore, the proposed guided self-supervised learning scheme can improve the baseline of simple self-supervised learning by 1%–1.27%. ConclusionThe final results show that the proposed CTMLP can replace CNNs or Transformers for a more efficient diagnosis of COVID-19. In addition, the additional pre-training framework was developed to make it more promising in clinical practice.

Semi-supervised graph convolutional networks for the domain adaptive recognition of thyroid nodules in cross-device ultrasound images.

Ultrasound plays a critical role in the early screening and diagnosis of cancers. Although deep neural networks have been widely investigated in the computer-aided diagnosis (CAD) of different medical images, diverse ultrasound devices, and image modalities pose challenges for clinical applications, especially in the recognition of thyroid nodules having various shapes and sizes. More generalized and extensible methods need to be developed for the cross-devices recognition of thyroidnodules. In this work, a semi-supervised graph convolutional deep learning framework is proposed for the domain adaptative recognition of thyroid nodules across several ultrasound devices. A deep classification network, trained on a source domain with a specific device, can be transferred to recognize thyroid nodules on the target domain with other devices, using only few manual annotated ultrasoundimages. This study presents a semi-supervised graph-convolutional-network-based domain adaptation framework, namely Semi-GCNs-DA. Based on the ResNet backbone, it is extended in three aspects for domain adaptation, that is, graph convolutional networks (GCNs) for the connection construction between source and target domains, semi-supervised GCNs for accurate target domain recognition, and pseudo labels for unlabeled target domains. Data were collected from 1498 patients comprising 12 108 images with or without thyroid nodules under three different ultrasound devices. Accuracy, Sensitivity and Specificity were used for the performanceevaluation. The proposed method was validated on six groups of data for a single source domain adaptation task, the mean Accuracy was 0.9719± 0.0023, 0.9928± 0.0022, 0.9353± 0.0105, 0.8727± 0.0021, 0.7596± 0.0045, 0.8482± 0.0092, which achieved better performance in comparison with the state-of-the-art. The proposed method was also validated on three groups of multiple source domain adaptation tasks. In particular, when using X60 and HS50 as the source domain data, and H60 as the target domain, it can achieve the Accuracy of 0.8829± 0.0079, Sensitivity of 0.9757± 0.0001, and Specificity of 0.7894± 0.0164. Ablation experiments also demonstrated the effectiveness of the proposedmodules. The developed Semi-GCNs-DA framework can effectively recognize thyroid nodules on different ultrasound devices. The developed semi-supervised GCNs can be further extended to the domain adaptation problems for other modalities of medicalimages.

PCCT: Progressive Class-Center Triplet Loss for Imbalanced Medical Image Classification.

Imbalanced training data in medical image diagnosis is a significant challenge for diagnosing rare diseases. For this purpose, we propose a novel two-stage Progressive Class-Center Triplet (PCCT) framework to overcome the class imbalance issue. In the first stage, PCCT designs a class-balanced triplet loss to coarsely separate distributions of different classes. Triplets are sampled equally for each class at each training iteration, which alleviates the imbalanced data issue and lays solid foundation for the successive stage. In the second stage, PCCT further designs a class-center involved triplet strategy to enable a more compact distribution for each class. The positive and negative samples in each triplet are replaced by their corresponding class centers, which prompts compact class representations and benefits training stability. The idea of class-center involved loss can be extended to the pair-wise ranking loss and the quadruplet loss, which demonstrates the generalization of the proposed framework. Extensive experiments support that the PCCT framework works effectively for medical image classification with imbalanced training images. On four challenging class-imbalanced datasets (two skin datasets Skin7 and Skin 198, one chest X-ray dataset ChestXray-COVID, and one eye dataset Kaggle EyePACs), the proposed approach respectively obtains the mean F1 score 86.20, 65.20, 91.32, and 87.18 over all classes and 81.40, 63.87, 82.62, and 79.09 for rare classes, achieving state-of-the-art performance and outperforming the widely used methods for the class imbalance issue.

Detecting Covid-19 and Other Lung Diseases with Deep Learning

Since December 2019, Covid-19, also known as severe acute respiratory syndrome (SARSCOV-2), has infected about 531 million individuals worldwide causing devastating consequences. Scientists, health professionals, and radiologists have all invested huge amounts of time, effort and resources in developing faster and accurate methods for detecting the virus as it spreads all over the world. The current gold standards for detecting covid-19 such as RT-PCR, POC, Droplet-based digital PCR (dPCR) and Immunoassays are not only expensive for low-income countries but also require substantial human expert knowledge which in turn makes them time-consuming and susceptible to manipulation. The study presented in this paper aims to address these issues by proposing a model to automatically detect COVID-19 in chest X-rays using Deep Learning techniques. Specifically, the model uses convolutional neural networks (CNNs), a deep learning technique, which has been shown to produced very good results when applied to medical image diagnosis problems. Our proposed model will therefore not only provide a cheaper, faster and accurate method of detecting COVID-19 but will also provide an easy to use web and mobile platform for non-experts. While current detection techniques such the RT-PCR are still considered as the most effective, our CNN-based model was also able to produce good results when applied to the COVID-19 radiography dataset. For instance, the model was able to detect COVID-19 from X-ray images with an accuracy rate of 90 percent. Furthermore, the model was also able to automatically detect other respiratory diseases such as viral pneumonia and lung opacity with an accuracy rate of over 90 percent. Given the potential of the automated detection of COVID-19 established in this study, future work will extend this work by incorporating automated search techniques such as genetic algorithms in the fine tuning of the model’s parameters in order to obtain an even higher accuracy rate.

Performance Investigation for Medical Image Evaluation and Diagnosis Using Machine-Learning and Deep-Learning Techniques

Today, medical image-based diagnosis has advanced significantly in the world. The number of studies being conducted in this field is enormous, and they are producing findings with a significant impact on humanity. The number of databases created in this field is skyrocketing. Examining these data is crucial to find important underlying patterns. Classification is an effective method for identifying these patterns. This work proposes a deep investigation and analysis to evaluate and diagnose medical image data using various classification methods and to critically evaluate these methods’ effectiveness. The classification methods utilized include machine-learning (ML) algorithms like artificial neural networks (ANN), support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), random forest (RF), Naïve Bayes (NB), logistic regression (LR), random subspace (RS), fuzzy logic and a convolution neural network (CNN) model of deep learning (DL). We applied these methods to two types of datasets: chest X-ray datasets to classify lung images into normal and abnormal, and melanoma skin cancer dermoscopy datasets to classify skin lesions into benign and malignant. This work aims to present a model that aids in investigating and assessing the effectiveness of ML approaches and DL using CNN in classifying the medical databases and comparing these methods to identify the most robust ones that produce the best performance in diagnosis. Our results have shown that the used classification algorithms have good results in terms of performance measures.

Location Fusion and Data Augmentation for Thoracic Abnormalites Detection in Chest X-Ray Images

The application of deep learning in medical image diagnosis has been widely studied recently. Unlike general objects, thoracic abnormalities in chest X-ray radiographs are much harder to label consistently by domain experts. Theproblem’s difficulty and inconsistency in data labeling lead to the downgraded performance of the robust deep learning models. This paper presents two methods to improve the accuracy of thoracic abnormalities detection in chest X-ray images. The first method is to fuse the locations of the same abnormality marked differently by radiologists. The second method is applying mosaic data augmentation in the training process to enrich the training data. Experiments on the VinDr-CXR chest X-ray data show that combining the two methods helps improve the predictive performance by up to 8% for F1-score and 9% for the mean average precision (MAP) score.

Smart Artificial Intelligence techniques using embedded band for diagnosis and combating COVID-19

Recently, COVID-19 virus spread to create a major impact in human body worldwide. The Corona virus, initiated by the SARS-CoV-2 virus, was known in China, December 2019 and affirmed a worldwide epidemic by the World Health Organization on 11 March 2020. The core aim of this research is to detect the spreading of COVID-19 virus and solve the problems in human lungs infection quickly. An Artificial Intelligence (AI) technique is a possibly controlling device in the battle against the corona virus epidemic. Recently, AI with computational techniques are utilized for COVID-19 virus with the building blocks of Deep Learning method using Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) is used to classify and identify the lung images affected region. These two algorithms used to diagnose COVID-19 infections rapidly. The AI applications against COVID-19 are Medical Imaging for Diagnosis, Lung delineation, Lesion measurement, Non-Invasive Measurements for Disease Tracking, Patient Outcome Prediction, Molecular Scale: from Proteins to Drug Development and Societal Scale: Epidemiology and Infodemiology.

Systematic assessment and review of techniques based on tumour detection in brain using MRI

ABSTRACT Detecting tumour with Magnetic Resonance imaging (MRI) has been promising in recent years. The current tumour detection strategies are used for visualising, digitising and modelling the images for medical diagnosis. Automatic tumour region segmentation from 3D MRIs is essential for monitoring, diagnosing, and planning the treatment remedies for the disease. However, manual outlining needs an anatomical understanding which requires more time and can be imprecise due to human error. Brain tumour is serious diseases whose exposure must be quick and precise. It is attained by the accomplishment of the automatic detection of the tumour using MRI. Several automatic strategies are employed in literary works, which utilise image segmentation. Here, 50 research papers are surveyed with brain tumour detection strategies like ANN, clustering, deep learning, edge-based techniques, optimisation and transformation strategies. Furthermore, complete exploration is done in terms of publication year, employed strategy, datasets utilised, execution tool, performance measures and its values. At last, the problems of classical strategies and its issues with classical brain tumour detection methods are illustrated to generate the contribution.

Multi-user multi-objective computation offloading for medical image diagnosis.

Computation offloading has effectively solved the problem of terminal devices computing resources limitation in hospitals by shifting the medical image diagnosis task to the edge servers for execution. Appropriate offloading strategies for diagnostic tasks are essential. However, the risk awareness of each user and the multiple expenses associated with processing tasks have been ignored in prior works. In this article, a multi-user multi-objective computation offloading for medical image diagnosis is proposed. First, the prospect theoretic utility function of each user is designed considering the delay, energy consumption, payment, and risk awareness. Second, the computation offloading problem including the above factors is defined as a distributed optimization problem, which with the goal of maximizing the utility of each user. The distributed optimization problem is then transformed into a non-cooperative game among the users. The exact potential game proves that the non-cooperative game has Nash equilibrium points. A low-complexity computation offloading algorithm based on best response dynamics finally is proposed. Detailed numerical experiments demonstrate the impact of different parameters and convergence in the algorithm on the utility function. The result shows that, compare with four benchmarks and four heuristic algorithms, the proposed algorithm in this article ensures a faster convergence speed and achieves only a 1.14% decrease in the utility value as the number of users increases.

Anomaly Detection in Chest X-rays Based on Dual-Attention Mechanism and Multi-Scale Feature Fusion

The efficient and automatic detection of chest abnormalities is vital for the auxiliary diagnosis of medical images. Many studies utilize computer vision and deep learning approaches involving symmetry and asymmetry concepts to detect chest abnormalities, and achieve promising findings. However, an accurate instance-level and multi-label detection of abnormalities in chest X-rays remains a significant challenge. Here, a novel anomaly detection method for symmetric chest X-rays using dual-attention and multi-scale feature fusion is proposed. Three aspects of our method should be noted in comparison with the previous approaches. We improved the deep neural network with channel-dimensional and spatial-dimensional attention to capture the abundant contextual features. We then used an optimized multi-scale learning framework for feature fusion to adapt to the scale variation in the abnormalities. Considering the influence of the data imbalance and other factors, we introduced a seesaw loss function to flexibly adjust the sample weights and enhance the model learning efficiency. The rigorous experimental evaluation of a public chest X-ray dataset with fourteen different types of abnormalities demonstrates that our model has a mean average precision of 0.362 and outperforms existing methods.

Challenges of Deep Learning in Medical Image Analysis—Improving Explainability and Trust

Deep learning has revolutionized the detection of diseases and is helping the healthcare sector break barriers in terms of accuracy and robustness to achieve efficient and robust computer-aided diagnostic systems. The application of deep learning techniques empowers automated AI-based utilities requiring minimal human supervision to perform any task related to medical diagnosis of fractures, tumors, and internal hemorrhage; preoperative planning; intra-operative guidance, etc. However, deep learning faces some major threats to the flourishing healthcare domain. This paper traverses the major challenges that the deep learning community of researchers and engineers faces, particularly in medical image diagnosis, like the unavailability of balanced annotated medical image data, adversarial attacks faced by deep neural networks and architectures due to noisy medical image data, a lack of trustability among users and patients, and ethical and privacy issues related to medical data. This study explores the possibilities of AI autonomy in healthcare by overcoming the concerns about trust that society has in autonomous intelligent systems.

Comment on: The AI and I: A Collaboration on Competence.

We are writing to bring attention to the limitations of using artificial intelligence (AI) in surgery. While AI has shown great potential in various fields, including medical imaging and diagnostics, its use in surgical procedures is still in its infancy and has significant limitations. First, AI algorithms require large amounts of data to be trained and tested, which is often not available in the surgical setting. This means that AI systems may not be able to adapt to the unique and complex situations that arise during surgery. Second, the accuracy and reliability of AI systems in surgery is still uncertain. Despite advances in technology, AI systems are still prone to errors and can miss important details that may have significant consequences during surgery. Finally, AI systems are not able to replace the critical thinking and decision making skills of trained surgeons. Surgeons need to be able to analyze a wide range of factors and make split-second decisions that AI systems may not be able to replicate. Overall, while AI has the potential to assist surgeons, its limitations should be carefully considered before implementing it in the surgical setting. Further research and development is needed to improve the accuracy and reliability of AI systems in surgery. Sincerely, Martin G. Tolsgaard and Lawrence Grierson P.S. We did not write any of this. An AI did and this was (close to) a Turing test. We typed the following into the OpenAI GTP-3 chatbot, which has recently been published:1write a letter about the limitations of AI in surgery in 200 words for a surgical journal from 2 scientists. Would you have noticed? Large language models such as GPT-3 can write manuscripts eloquently as seen above and even perform reasonably well on USMLE exams.2 Examples of super-human performance in medical imaging diagnosis have already been published for several years.3 However, now these models are beginning to carve further into human domains of expertise by imitating clinical reasoning, surgical expertise, and academic writing—something that we consider core to what makes us different from AI. This leads us to question the nature and understanding of competence. How will our understanding of what it means to write well academically or be an expert surgeon change when an AI sometimes surpasses our own performance? Narrowly focusing on limitations or benefits of AI may not advance our understanding of what surgeons should be able to do in the future and how. Instead, we should consider exploring when and under what circumstances human-AI collaboration works, for whom and why. We need to turn the scientific discourse away from focusing on how AI can replace clinicians and instead explore how best to support their learning and performances through collective competence. Yet, this requires us to take the science of learning and clinical reasoning into account, which is rarely considered in existing AI research.4

A Novel Convolutional Neural Network Model Based on Beetle Antennae Search Optimization Algorithm for Computerized Tomography Diagnosis.

Convolutional neural networks (CNNs) are widely used in the field of medical imaging diagnosis but have the disadvantages of slow training speed and low diagnostic accuracy due to the initialization of parameters before training. In this article, a CNN optimization method based on the beetle antennae search (BAS) optimization algorithm is proposed. The method optimizes the initial parameters of the CNN through the BAS optimization algorithm. Based on this optimization approach, a novel CNN model with a pretrained BAS optimization algorithm was developed and applied to the analysis and diagnosis of medical imaging data for intracranial hemorrhage. Experimental results on 330 test images show that the proposed method has a better diagnostic performance than the traditional CNN. The proposed method achieves a diagnostic accuracy of 93.9394% and 100% recall, and the diagnosis of 66 human head computerized tomography image data only takes 0.1596 s. Moreover, the proposed method has more advantages than the three other optimization algorithms.

Application of Fuzzy Deep Neural Networks for Covid 19 diagnosis through chest Radiographs

Background: The increasing number of COVID-19 patients around the world and the limited number of detection kits pose a challenge in determining the presence of the disease. Imaging modalities such as X-rays are commonly used because they are readily available and cost-effective. Deep learning has proved to be an excellent tool because of the abundance of online medical images in various medical modalities, such as X-Ray, computerized tomography (CT) Scan, and magnetic resonance imaging (MRI). A large number of medical research projects have been proposed and launched since early 2020 due to the overwhelming use of deep learning techniques in medical imaging. Methods: We have used fuzzy logic and deep learning to determine if chest X-ray images belong to people who have pneumonia related to COVID-19 and people who have interstitial pneumonias that aren't related to COVID-19. Results: In comparison to the current literature, the proposed transfer learning approach is more successful. It is possible to classify covid, viral, and bacterial pneumonia or a healthy patient using ResNet 18 Architecture's four-class classifiers. The proposed method achieved a 97% classification accuracy, 96% precision, and 98% recall in the case of COVID-19 detection using chest X-ray images, which demonstrates the importance of deep learning in medical image diagnosis. Furthermore, the results demonstrate that the proposed technique has the maximum sensitivity rate, with 97.1% ratio. Finally, with a 97.47% F1-score rate, the proposed strategy yields the highest value when compared to the others. Conclusions: DeepLearning techniques and fuzzy features resulted in an improved classification ability, with an accuracy rate of up to 97.7% using ResNet 18, which is a better value when compared to the remaining techniques. Classification of COVID-19 scans and other pneumonia cases have been done successfully by demonstrating the potential for applying such deep learning techniques in the near future.

AAPM task group report 273: Recommendations on best practices for ai and machine learning for computer-aided diagnosis in medical imaging.

Rapid advances in artificial intelligence (AI) and machine learning, and specifically in deep learning (DL) techniques, have enabled broad application of these methods in health care. The promise of the DL approach has spurred further interest in computer-aided diagnosis (CAD) development and applications using both 'traditional' machine learning methods and newer DL-based methods. We use the term CAD-AI to refer to this expanded clinical decision support environment that uses traditional and DL-based AI methods. Numerous studies have been published to date on the development of machine learning tools for computer-aided, or AI-assisted, clinical tasks. However, most of these machine learning models are not ready for clinical deployment. It is of paramount importance to ensure that a clinical decision support tool undergoes proper training and rigorous validation of its generalizability and robustness before adoption for patient care in the clinic. To address these important issues, the American Association of Physicists in Medicine (AAPM) Computer-Aided Image Analysis Subcommittee (CADSC) is charged, in part, to develop recommendations on practices and standards for the development and performance assessment of computer-aided decision support systems. The committee has previously published two opinion papers on the evaluation of CAD systems and issues associated with user training and quality assurance of these systems in the clinic. With machine learning techniques continuing to evolve and CAD applications expanding to new stages of the patient care process, the current task group report considers the broader issues common to the development of most, if not all, CAD-AI applications and their translation from the bench to the clinic. The goal is to bring attention to the proper training and validation of machine learning algorithms that may improve their generalizability and reliability and accelerate the adoption of CAD-AI systems for clinical decision support. This article is protected by copyright. All rights reserved.

Fiber Optic Plate Coupled Pb-Free Perovskite X-ray Camera Featuring Low-Dose-Rate Imaging toward Dental Diagnosis.

Copper-based halide perovskites have been considered as promising scintillators. However, they still cannot meet the requirement of low-dose-rate X-ray imaging in medical diagnosis. Herein, we design a fiber optic plate (FOP) coupled perovskite X-ray camera to reduce the dose rate toward dental X-ray imaging. Tl doped Cs3Cu2I5 prepared via molten salt reaction has a high light yield of 72,000 photons/MeV, resulting from Tl10/Tl20-self-trapped hole emissions. After FOP coupling, the pulp cavity, root canal, dentin and root canal file can be clearly observed under a low dose rate as low as 3 μGyair s-1, which is absolutely lower than the required 5.5 μGyair s-1 for commercial intraoral dental sensors. The realization of such a low dose rate is attributed to the high coupling efficiency of 75% for the FOP and the high brightness of 262 lm m-2 for the scintillation screen. This designed portable X-ray camera shows its huge potential in intraoral dental X-ray imaging.

Deep Capsule Residual Networks for Better Diagnosis Rate in Medical Noisy Images

With the advent of Machine and Deep Learning algorithms, medical image diagnosis has a new perception of diagnosis and clinical treatment. Regrettably, medical images are more susceptible to capturing noises despite the peak in intelligent imaging techniques. However, the presence of noise images degrades both the diagnosis and clinical treatment processes. The existing intelligent methods suffer from the deficiency in handling the diverse range of noise in the versatile medical images. This paper proposes a novel deep learning network which learns from the substantial extent of noise in medical data samples to alleviate this challenge. The proposed deep learning architecture exploits the advantages of the capsule network, which is used to extract correlation features and combine them with redefined residual features. Additionally, the final stage of dense learning is replaced with powerful extreme learning machines to achieve a better diagnosis rate, even for noisy and complex images. Extensive experimentation has been conducted using different medical images. Various performances such as Peak-Signal-To-Noise Ratio (PSNR) and Structural-Similarity-Index-Metrics (SSIM) are compared with the existing deep learning architectures. Additionally, a comprehensive analysis of individual algorithms is analyzed. The experimental results prove that the proposed model has outperformed the other existing algorithms by a substantial margin and proved its supremacy over the other learning models.

Progres of deep learning based quantitative MRI reconstruction methods

磁共振成像是一种应用广泛的无创医学成像方法，因其丰富的软组织对比度可以成像人体几乎所有内部结构，包括器官、骨骼、肌肉和血管，已成为临床医学影像诊断的利器。然而磁共振成像存在两大公认的瓶颈：成像速度慢、扫描操作烦琐。深度学习给磁共振成像带来莫大的契机，给传统磁共振加速成像带来新的可能。鉴于该领域的快速发展性质，本文旨在总结文献中报道的大量深度学习和磁共振图像重建相结合的方法，以更好地了解该领域。本文简单介绍磁共振成像在多通道线圈接收的并行加速和压缩感知加速下的深度学习重建方法，其中单对比度图像可通过多通道接收线圈提供的冗余度用于加速，多对比度图像可额外使用不同对比度图像这一维度用于加速，动态图像与多对比度图像类似可额外使用时间维度用于加速，本文也将介绍深度学习在这些方面的发展。随着磁共振成像近年来由定性多对比度成像向定量多参数成像的发展，其中定量成像的图像中可内含多对比度图像，如何借用深度学习提供的能力将定性多对比度图像映射到参数图也是一个难点，近年来这一方向也获得了较快的发展，文中也将针对这方面内容进行调研并介绍。针对上述内容，分别介绍国际研究现状和国内研究现状，拟更好地总结国内外研究的发展的异同和趋势。最后对深度学习助力定量磁共振成像方面进行了展望。;Magnetic resonance imaging(MRI) is a commonly-used non-invasive medical imaging method. Due to richable soft tissue-related contrast features of human body, medical imaging diagnosis is beneficial from more internal structures-contextual image in clinic, including its organs, bones, muscles, and blood vessels. However, two sorts of bottlenecks of MRI like slow scanning speed and labor-intensive scanning operation are required to be resolved beyond the constraints of hardware and existing techniques. Current MRI has been facilitating in terms of the emerging deep learning based medical imaging technique. To optimize its acquisition time, parallel imaging and compressed sensing combined with the use of multi-array coils, conventional MRI mainly depends on the hardware improvements and new compactable sequence design. First, literature review of deep learning-based MR reconstruction methods are analyzed, including such deep learning issues originated from 1) integration of parallel imaging and compressed sensing of MRI and 2) acceleration of multi-contrast images and dynamic images of single-contrast image. Deep learning-based reconstruction models is oriented to get their generalizability incorporated with multiple datasets, in which MRI data are challenged for multiple factors like its relevance of centers, models, and field strengths. Two decadal development of quantitative MRI technique can provide pixel-level characterization of intrinsic tissue quantitative parameters, such as T1, T2, and ADC. Generally, quantitative MRI is often required to get multiple weighted images under different parameters, and quantitative tissue parameters can be generated via signal model-required pixel-level nonlinear fitting. Compared to the acquisition of contrast images, acquisition and reconstruction duration has become more longer. To accelerate the acquisition and reconstruction or facilitate accurate mapping, extension ability of deep learning has been developing for the reconstruction of MRI fast imaging. Apart from conventional single parameter mapping method, simultaneous multi-parameter mapping techniques have leaked out, such as MR fingerprinting. Compared to single parameter mapping, multi-parameter mapping can be synchronized and accelerated, and more efficient acquisition and inherent coregistration can be used to deal with such complicated reconstruction. Deep learning technique can be as a multifaceted tool to simplify the reconstruction and speed up the acquisition. However, it is still challenged for such constraints like deep learning algorithms and large amounts of training data. Deep learning methods are required to be melted into magnetic resonance physical models and traditional reconstruction algorithms to a certain extent, and to improve the interpretability of the model and alleviate the impact on large datasets, such methods of data enhancement, weakly supervised learning, unsupervised learning, and transfer learning can be involved in as well. A quantitative MRI-relevant unsupervised network can be potentially used for training in terms of different parameter mapping sequences, which can greatly shrink the amount of data samples further.

Highly sensitive microfiber ultrasound sensor for photoacoustic imaging

An ultrasound wave is a kind of acoustic signal with a frequency greater than 20 kHz, which is widely used in diverse fields such as medical imaging diagnosis, nondestructive testing and resource exploration. A variety of ultrasound sensors have been developed for ultrasound detection. Particularly for photoacoustic imaging, specialized ultrasound sensors with high sensitivity, small size, and broad bandwidth are needed. However, achieving such sensor performance still poses a great challenge to the current state-of-the-art in ultrasound sensor technology. A recent work published in <italic>Opto-Electronic Advances</italic> (DOI: 10.29026/oea.2022.200076) proposes a microfiber-based ultrasound sensor that breaks the limitations of existing ultrasound sensors. Benefiting from the large evanescent field characteristic of microfiber, combined with the coherent detection technology, the proposed sensor realized highly sensitive ultrasound detection and demonstrated excellent performance in high-resolution photoacoustic imaging. The highly sensitive and miniaturized microfiber ultrasound sensor provides a competitive alternative for various applications, such as endoscopic photoacoustic imaging of the intestinal tract and blood vessels in animals.

Transformer-based personalized attention mechanism for medical images with clinical records

In medical image diagnosis, identifying the attention region, i.e., the region of interest for which the diagnosis is made, is an important task. Various methods have been developed to automatically identify target regions from given medical images. However, in actual medical practice, the diagnosis is made based on both the images and various clinical records. Consequently, pathologists examine medical images with prior knowledge of the patients and the attention regions may change depending on the clinical records. In this study, we propose a method, called the Personalized Attention Mechanism (PersAM) method, by which the attention regions in medical images according to the clinical records. The primary idea underlying the PersAM method is the encoding of the relationships between medical images and clinical records using a variant of the Transformer architecture. To demonstrate the effectiveness of the PersAM method, we applied it to a large-scale digital pathology problem involving identifying the subtypes of 842 malignant lymphoma patients based on their gigapixel whole-slide images and clinical records.