A survey on deep learning for skin lesion segmentation

Skin cancer is a major public health problem that could benefit from computer-aided diagnosis to reduce the burden of this common disease. Skin lesion segmentation from images is an important step toward achieving this goal. However, the presence of natural and artificial artifacts (e.g., hair and air bubbles), intrinsic factors (e.g., lesion shape and contrast), and variations in image acquisition conditions make skin lesion segmentation a challenging task. Recently, various researchers have explored the applicability of deep learning models to skin lesion segmentation. In this survey, we cross-examine 177 research papers that deal with deep learning-based segmentation of skin lesions. We analyze these works along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules, and losses), and evaluation aspects (data annotation requirements and segmentation performance). We discuss these dimensions both from the viewpoint of select seminal works, and from a systematic viewpoint, examining how those choices have influenced current trends, and how their limitations should be addressed. To facilitate comparisons, we summarize all examined works in a comprehensive table as well as an interactive table available online³.     

A Deep Learning Model for Idiopathic Osteosclerosis Detection on Panoramic Radiographs

ObjectiveThe purpose of the study was to create an artificial intelligence (AI) system for detecting idiopathic osteosclerosis (IO) on panoramic radiographs for automatic, routine, and simple evaluations.Subject and MethodsIn this study, a deep learning method was carried out with panoramic radiographs obtained from healthy patients. A total of 493 anonymized panoramic radiographs were used to develop the AI system (CranioCatch, Eskisehir, Turkey) for the detection of IOs. The panoramic radiographs were acquired from the radiology archives of the Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University. GoogLeNet Inception v2 model implemented with TensorFlow library was used for the detection of IOs. Confusion matrix was used to predict model achievements.ResultsFifty IOs were detected accurately by the AI model from the 52 test images which had 57 IOs. The sensitivity, precision, and F-measure values were 0.88, 0.83, and 0.86, respectively.ConclusionDeep learning-based AI algorithm has the potential to detect IOs accurately on panoramic radiographs. AI systems may reduce the workload of dentists in terms of diagnostic efforts.     

Deep learning driven colorectal lesion detection in gastrointestinal endoscopic and pathological imaging

Colorectal cancer has the second highest incidence of malignant tumors and is the fourth leading cause of cancer deaths in China. Early diagnosis and treatment of colorectal cancer will lead to an improvement in the 5-year survival rate, which will reduce medical costs. The current diagnostic methods for early colorectal cancer include excreta, blood, endoscopy, and computer-aided endoscopy. In this paper, research on image analysis and prediction of colorectal cancer lesions based on deep learning is reviewed with the goal of providing a reference for the early diagnosis of colorectal cancer lesions by combining computer technology, 3D modeling, 5G remote technology, endoscopic robot technology, and surgical navigation technology. The findings will supplement the research and provide insights to improve the cure rate and reduce the mortality of colorectal cancer.     

Artificial intelligence ecosystem for computational psychiatry: Ideas to practice

Computational psychiatry is an emerging field that not only explores the biological basis of mental illness but also considers the diagnoses and identifies the underlying mechanisms. One of the key strengths of computational psychiatry is that it may identify patterns in large datasets that are not easily identifiable. This may help researchers develop more effective treatments and interventions for mental health problems. This paper is a narrative review that reviews the literature and produces an artificial intelligence ecosystem for computational psychiatry. The artificial intelligence ecosystem for computational psychiatry includes data acquisition, preparation, modeling, application, and evaluation. This approach allows researchers to integrate data from a variety of sources, such as brain imaging, genetics, and behavioral experiments, to obtain a more complete understanding of mental health conditions. Through the process of data preprocessing, training, and testing, the data that are required for model building can be prepared. By using machine learning, neural networks, artificial intelligence, and other methods, researchers have been able to develop diagnostic tools that can accurately identify mental health conditions based on a patient’s symptoms and other factors. Despite the continuous development and breakthrough of computational psychiatry, it has not yet influenced routine clinical practice and still faces many challenges, such as data availability and quality, biological risks, equity, and data protection. As we move progress in this field, it is vital to ensure that computational psychiatry remains accessible and inclusive so that all researchers may contribute to this significant and exciting field.     

One model is all you need: Multi-task learning enables simultaneous histology image segmentation and classification

The recent surge in performance for image analysis of digitised pathology slides can largely be attributed to the advances in deep learning. Deep models can be used to initially localise various structures in the tissue and hence facilitate the extraction of interpretable features for biomarker discovery. However, these models are typically trained for a single task and therefore scale poorly as we wish to adapt the model for an increasing number of different tasks. Also, supervised deep learning models are very data hungry and therefore rely on large amounts of training data to perform well. In this paper, we present a multi-task learning approach for segmentation and classification of nuclei, glands, lumina and different tissue regions that leverages data from multiple independent data sources. While ensuring that our tasks are aligned by the same tissue type and resolution, we enable meaningful simultaneous prediction with a single network. As a result of feature sharing, we also show that the learned representation can be used to improve the performance of additional tasks via transfer learning, including nuclear classification and signet ring cell detection. As part of this work, we train our developed Cerberus model on a huge amount of data, consisting of over 600 thousand objects for segmentation and 440 thousand patches for classification. We use our approach to process 599 colorectal whole-slide images from TCGA, where we localise 377 million, 900 thousand and 2.1 million nuclei, glands and lumina respectively. We make this resource available to remove a major barrier in the development of explainable models for computational pathology.     

Artificial intelligence: A joint narrative on potential use in pediatric stem and immune cell therapies and regenerative medicine

Artificial Intelligence (AI) reflects the intelligence exhibited by machines and software. It is a highly desirable academic field of many current fields of studies. Leading AI researchers describe the field as “the study and design of intelligent agents”. McCarthy invented this term in 1955 and defined it as “the science and engineering of making intelligent machines”. The central goals of AI research are reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects. In fact the multidisplinary AI field is considered to be rather interdisciplinary covering numerous number of sciences and professions, including computer science, psychology, linguistics, philosophy and neurosciences. The field was founded on the claim that a central intellectual property of humans, intelligence-the sapience of Homo Sapiens “can be so precisely described that a machine can be made to simulate it”. This raises philosophical issues about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence. Artificial Intelligence has been the subject of tremendous optimism but has also suffered stunning setbacks.The goal of this narrative is to review the potential use of AI approaches and their integration into pediatric cellular therapies and regenerative medicine. Emphasis is placed on recognition and application of AI techniques in the development of predictive models for personalized treatments with engineered stem cells, immune cells and regenerated tissues in adults and children. These intelligent machines could dissect the whole genome and isolate the immune particularities of individual patient’s disease in a matter of minutes and create the treatment that is customized to patient’s genetic specificity and immune system capability. AI techniques could be used for optimization of clinical trials of innovative stem cell and gene therapies in pediatric patients by precise planning of treatments, predicting clinical outcomes, simplifying recruitment and retention of patients, learning from input data and applying to new data, thus lowering their complexity and costs. Complementing human intelligence with machine intelligence could have an exponentially high impact on continual progress in many fields of pediatrics. However how long before we could see the real impact still remains the big question. The most pertinent question that remains to be answered therefore, is can AI effectively and accurately predict properties of newer DDR strategies?The goal of this article is to review the use of AI method for cellular therapy and regenerative medicine and emphasize its potential to further the progress in these fields of medicine.     

A semi-supervised multi-task learning framework for cancer classification with weak annotation in whole-slide images

Cancer region detection (CRD) and subtyping are two fundamental tasks in digital pathology image analysis. The development of data-driven models for CRD and subtyping on whole-slide images (WSIs) would mitigate the burden of pathologists and improve their accuracy in diagnosis. However, the existing models are facing two major limitations. Firstly, they typically require large-scale datasets with precise annotations, which contradicts with the original intention of reducing labor effort. Secondly, for the subtyping task, the non-cancerous regions are treated as the same as cancerous regions within a WSI, which confuses a subtyping model in its training process. To tackle the latter limitation, the previous research proposed to perform CRD first for ruling out the non-cancerous region, then train a subtyping model based on the remaining cancerous patches. However, separately training ignores the interaction of these two tasks, also leads to propagating the error of the CRD task to the subtyping task. To address these issues and concurrently improve the performance on both CRD and subtyping tasks, we propose a semi-supervised multi-task learning (MTL) framework for cancer classification. Our framework consists of a backbone feature extractor, two task-specific classifiers, and a weight control mechanism. The backbone feature extractor is shared by two task-specific classifiers, such that the interaction of CRD and subtyping tasks can be captured. The weight control mechanism preserves the sequential relationship of these two tasks and guarantees the error back-propagation from the subtyping task to the CRD task under the MTL framework. We train the overall framework in a semi-supervised setting, where datasets only involve small quantities of annotations produced by our minimal point-based (min-point) annotation strategy. Extensive experiments on four large datasets with different cancer types demonstrate the effectiveness of the proposed framework in both accuracy and generalization.     

Deep learning-assisted diagnosis of femoral trochlear dysplasia based on magnetic resonance imaging measurements

BACKGROUND Femoral trochlear dysplasia(FTD) is an important risk factor for patellar instability. Dejour classification is widely used at present and relies on standard lateral X-rays, which are not common in clinical work. Therefore, magnetic resonance imaging(MRI) has become the first choice for the diagnosis of FTD. However, manually measuring is tedious, time-consuming, and easily produces great variability.AIM To use artificial intelligence(AI) to assist diagnosing FTD on MRI images and to ...     

基于深度学习技术的乳腺智能检测系统在触诊阴性乳腺肿瘤诊断中的应用

目的 拟探讨基于深度学习技术的乳腺X线智能检测系统在临床触诊阴性乳腺肿瘤诊断中的应用价值.方法 回顾性收集2014年1月至2016年12月期间就诊于陕西省肿瘤医院的临床触诊阴性乳腺肿瘤患者322例,均手术治疗且临床病理资料齐全.使用MammoWorks?乳腺智能检测系统对所有入组患者乳腺X线图片进行分析,以术后病理结果...     

Deep neural network models for computational histopathology: A survey

Histopathological images contain rich phenotypic information that can be used to monitor underlying mechanisms contributing to disease progression and patient survival outcomes. Recently, deep learning has become the mainstream methodological choice for analyzing and interpreting histology images. In this paper, we present a comprehensive review of state-of-the-art deep learning approaches that have been used in the context of histopathological image analysis. From the survey of over 130 papers, we review the field’s progress based on the methodological aspect of different machine learning strategies such as supervised, weakly supervised, unsupervised, transfer learning and various other sub-variants of these methods. We also provide an overview of deep learning based survival models that are applicable for disease-specific prognosis tasks. Finally, we summarize several existing open datasets and highlight critical challenges and limitations with current deep learning approaches, along with possible avenues for future research.     

A review on deep-learning algorithms for fetal ultrasound-image analysis

Deep-learning (DL) algorithms are becoming the standard for processing ultrasound (US) fetal images. A number of survey papers in the field is today available, but most of them are focusing on a broader area of medical-image analysis or not covering all fetal US DL applications. This paper surveys the most recent work in the field, with a total of 153 research papers published after 2017. Papers are analyzed and commented from both the methodology and the application perspective. We categorized the papers into (i) fetal standard-plane detection, (ii) anatomical structure analysis and (iii) biometry parameter estimation. For each category, main limitations and open issues are presented. Summary tables are included to facilitate the comparison among the different approaches. In addition, emerging applications are also outlined. Publicly-available datasets and performance metrics commonly used to assess algorithm performance are summarized, too. This paper ends with a critical summary of the current state of the art on DL algorithms for fetal US image analysis and a discussion on current challenges that have to be tackled by researchers working in the field to translate the research methodology into actual clinical practice.     

Continual learning strategies for cancer-independent detection of lymph node metastases

Recently, large, high-quality public datasets have led to the development of convolutional neural networks that can detect lymph node metastases of breast cancer at the level of expert pathologists. Many cancers, regardless of the site of origin, can metastasize to lymph nodes. However, collecting and annotating high-volume, high-quality datasets for every cancer type is challenging. In this paper we investigate how to leverage existing high-quality datasets most efficiently in multi-task settings for closely related tasks. Specifically, we will explore different training and domain adaptation strategies, including prevention of catastrophic forgetting, for breast, colon and head-and-neck cancer metastasis detection in lymph nodes.Our results show state-of-the-art performance on colon and head-and-neck cancer metastasis detection tasks. We show the effectiveness of adaptation of networks from one cancer type to another to obtain multi-task metastasis detection networks. Furthermore, we show that leveraging existing high-quality datasets can significantly boost performance on new target tasks and that catastrophic forgetting can be effectively mitigated.Last, we compare different mitigation strategies.     

人工智能辅助床旁超声诊断肾创伤的基础研究

目的探讨基于卷积神经网络（CNN）构建的人工智能辅助诊断模型对肾钝性创伤超声诊断的应用价值。 方法建立不同程度动物肾创伤模型,通过床旁超声仪采集正常肾及创伤肾超声图片,分成训练集及测试集,根据造模位置和超声造影结果,手动勾画出肾轮廓,采用3折交叉验证进行分类训练及测试。绘制受试者工作特征（ROC）曲线,计算人工智能辅助诊断模型的敏感度、特异度、准确性和曲线下面积（AUC）。 结果采集正常肾图片共1737张,各级别创伤肾图片共2125张,经过对测试集的验证,该模型可自动对肾创伤有无进行分类,对肾创伤诊断的平均敏感度为73%、平均特异度为85%、平均准确性为79%、AUC为0.80,诊断价值较高。 结论基于CNN构建的深度学习模型辅助床旁超声仪在诊断肾创伤有无分类中取得了较满意的结果。     

基于深度学习的肝外伤超声图像自动识别模型

目的建立基于深度学习的卷积神经网络肝损伤模型（CNLDM）,并评估其对肝实质挫裂伤的诊断价值。 方法通过动物实验获得2009张含有肝实质挫裂伤超声图像及1302张正常肝超声图像,作为模型的训练集和验证集。回顾性收集2015年1月至2021年4月解放军总医院第一医学中心确诊存在肝实质挫裂伤的超声图像153张,以及81张不含肝实质挫裂伤的肝超声图像,作为模型的外部测试集。6名不同年资医师分别对测试集图像数据进行判读。使用受试者操作特征（ROC）曲线及决策曲线分析（DCA）检验模型效能,比较不同年资医师与CNLDM模型预测肝实质挫裂伤的敏感度、特异度、准确性、阴性预测值及阳性预测值。 结果CNLDM模型诊断效能（敏感度为80%,特异度为77%,阳性预测值为87%,阴性预测值为66%）优于低年资医师组（敏感度为61%,特异度为75%,阳性预测值为82%,阴性预测值为51%）,略差于高年资医师组（敏感度为84%,特异度为86%,阳性预测值为92%,阴性预测值为75%）,差异具有统计学意义（H=15.306,P＜0.001;H=3.289,P＜0.001）,而模型效能与中年资医师组接近,差异无统计学意义（P＞0.05）。DCA显示模型在阈值0.4~0.6之间有较好的测试集收益。 结论基于超声的人工智能模型可以较为准确地区分正常肝与含有肝实质挫裂伤的异常肝,对进一步指导临床诊治工作具有重要的意义。     

Trustworthy learning with (un)sure annotation for lung nodule diagnosis with CT

Recent evolution in deep learning has proven its value for CT-based lung nodule classification. Most current techniques are intrinsically black-box systems, suffering from two generalizability issues in clinical practice. First, benign-malignant discrimination is often assessed by human observers without pathologic diagnoses at the nodule level. We termed these data as “unsure-annotation data”. Second, a classifier does not necessarily acquire reliable nodule features for stable learning and robust prediction with patch-level labels during learning. In this study, we construct a sure-annotation dataset with pathologically-confirmed labels and propose a collaborative learning framework to facilitate sure nodule classification by integrating unsure-annotation data knowledge through nodule segmentation and malignancy score regression. A loss function is designed to learn reliable features by introducing interpretability constraints regulated with nodule segmentation maps. Furthermore, based on model inference results that reflect the understanding from both machine and experts, we explore a new nodule analysis method for similar historical nodule retrieval and interpretable diagnosis. Detailed experimental results demonstrate that our approach is beneficial for achieving improved performance coupled with trustworthy model reasoning for lung cancer prediction with limited data. Extensive cross-evaluation results further illustrate the effect of unsure-annotation data for deep-learning based methods in lung nodule classification.     

Application of artificial neural networks in detection and diagnosis of gastrointestinal and liver tumors

As a form of artificial intelligence, artificial neural networks (ANNs) have the advantages of adaptability, parallel processing capabilities, and non-linear processing. They have been widely used in the early detection and diagnosis of tumors. In this article, we introduce the development, working principle, and characteristics of ANNs and review the research progress on the application of ANNs in the detection and diagnosis of gastrointestinal and liver tumors.     

Deep learning prediction of diffusion MRI data with microstructure-sensitive loss functions

Deep learning prediction of diffusion MRI (DMRI) data relies on the utilization of effective loss functions. Existing losses typically measure the signal-wise differences between the predicted and target DMRI data without considering the quality of derived diffusion scalars that are eventually utilized for quantification of tissue microstructure. Here, we propose two novel loss functions, called microstructural loss and spherical variance loss, to explicitly consider the quality of both the predicted DMRI data and derived diffusion scalars. We apply these loss functions to the prediction of multi-shell data and enhancement of angular resolution. Evaluation based on infant and adult DMRI data indicates that both microstructural loss and spherical variance loss improve the quality of derived diffusion scalars.     

机器学习在急诊医学中应用的研究进展及展望

随着信息技术及医疗数据信息化的不断发展,越来越多的临床医生认识到人工智能或将彻底改变医学实践。机器学习可对大量医疗数据进行学习,探索数据集中的依赖关系,从而形成相应的医学模型;模型可对新的数据进行快速准确预测,有利于疾病早期诊断分级、辅助制定临床决策等。急诊医学面临着医疗资源相对短缺、急危重症患者识别及快速诊治需求等现状。在大数据时代,以临床需求为导向,机器学习为手段的智慧医疗或将成为解决上述问题的关键之一。     

基于灰阶超声联合剪切波弹性成像的影像组学模型诊断乳腺癌腋窝淋巴结转移的临床价值

目的 基于灰阶超声（US）、剪切波弹性成像（SWE）图像特征及临床、病理指标构建双模态影像组学模型,探讨其对乳腺癌腋窝淋巴结转移的诊断价值。方法 选取于我院行乳腺癌手术治疗患者306例,按照7∶3比例随机分为训练集（214例）和验证集（92例）,基于术前US和SWE图像分别进行感兴趣区分割和特征提取。应用最小绝对收缩和选择算子（LASSO）算法筛选关键特征并分别构建US、SWE影像组学标记物（US-RIS、SWE-RIS）。采用单因素和多因素Logistic回归在临床、病理指标和RIS中筛选变量并构建单模态US、SWE影像组学模型及双模态影像组学模型;绘制受试者工作特征（ROC）曲线分析并比较各影像组学模型、超声医师对乳腺癌腋窝淋巴结转移的诊断效能;绘制决策曲线评估各影像组学模型的临床实用价值;绘制校准曲线分析双模态影像组学模型预测结果与实际结果的一致性。结果 基于LASSO算法筛选出13个关键US图像特征和17个关键SWE图像特征,分别构建US-RIS和SWE-RIS。单因素和多因素Logistic回归分析显示,BI-RADS分类、肿瘤分类、US-RIS、SWE-RIS均为乳腺癌腋窝...