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arXiv 2405.09934cs.CVcs.AI
使用Fréchet域距离检测数字病理多示例学习中的域偏移
Detecting Domain Shift in Multiple Instance Learning for Digital Pathology Using Fréchet Domain Distance
Center for Medical Imaging Science and Visualization, Linköping University, University Hospital（林雪平大学 医学影像科学与可视化中心，大学医院）；Department of Science and Technology, Linköping University（林雪平大学 科学技术系）；Department of Biomedical and Clinical Sciences, Linköping University（林雪平大学 生物医学与临床科学系）；Sectra AB（塞克特拉公司）
Milda Pocevičiūtė, Gabriel Eilertsen, Stina Garvin, Claes Lundström
多示例学习（Multiple-instance learning, MIL）是数字病理应用中极具吸引力的方法，因为它能降低数据收集和标注相关的成本。然而，目前尚不清楚MIL对临床真实场景下的域偏移（即可能对性能产生负面影响的数据分布差异）的敏感程度如何，也不清楚现有的域偏移检测指标是否能很好地适配这类算法。我们训练了一种基于注意力的MIL算法，用于分类淋巴结的全切片图像是否包含乳腺肿瘤转移灶。该算法在来自另一家国家医院的数据，以及对应不同域偏移程度的该数据的多个子集上进行了评估。我们的贡献包括：证明了数字病理领域的MIL会受到临床真实数据差异的影响；评估了MIL模型中哪些特征最适合检测性能变化；提出了一种名为Fréchet Domain Distance（FDD，Fréchet域距离）的无监督指标用于量化域偏移。我们通过与分类性能变化的平均皮尔逊相关系数来评估偏移度量的性能，其中FDD在10折交叉验证模型上达到了0.70。基线方法包括Deep ensemble（深度集成）、Difference of Confidence（置信度差异）和Representation shift（表示偏移），它们的平均皮尔逊相关系数分别为0.45、-0.29和0.56。对于需要验证MIL系统在新站点部署时是否可能可靠运行，且无需病理学家提供任何额外标注的医疗机构和供应商而言，FDD可能是一种有价值的工具。
英文摘要：
Multiple-instance learning (MIL) is an attractive approach for digital pathology applications as it reduces the costs related to data collection and labelling. However, it is not clear how sensitive MIL is to clinically realistic domain shifts, i.e., differences in data distribution that could negatively affect performance, and if already existing metrics for detecting domain shifts work well with these algorithms. We trained an attention-based MIL algorithm to classify whether a whole-slide image of a lymph node contains breast tumour metastases. The algorithm was evaluated on data from a hospital in a different country and various subsets of this data that correspond to different levels of domain shift. Our contributions include showing that MIL for digital pathology is affected by clinically realistic differences in data, evaluating which features from a MIL model are most suitable for detecting changes in performance, and proposing an unsupervised metric named Fréchet Domain Distance (FDD) for quantification of domain shifts. Shift measure performance was evaluated through the mean Pearson correlation to change in classification performance, where FDD achieved 0.70 on 10-fold cross-validation models. The baselines included Deep ensemble, Difference of Confidence, and Representation shift which resulted in 0.45, -0.29, and 0.56 mean Pearson correlation, respectively. FDD could be a valuable tool for care providers and vendors who need to verify if a MIL system is likely to perform reliably when implemented at a new site, without requiring any additional annotations from pathologists.
Deep learning (DL) has shown great potential in digital pathology applications. The robustness of a diagnostic DL-based solution is essential for safe clinical deployment. In this work we evaluate if adding uncertainty estimates for DL predictions in digital pathology could result in increased value for the clinical applications, by boosting the general predictive performance or by detecting mispredictions. We compare the effectiveness of model-integrated methods (MC dropout and Deep ensembles) with a model-agnostic approach (Test time augmentation, TTA). Moreover, four uncertainty metrics are…
Google Brain（谷歌大脑）；Google Inc（谷歌公司）；Verily Life Sciences（Verily生命科学）
Each year, the treatment decisions for more than 230,000 breast cancer patients in the U.S. hinge on whether the cancer has metastasized away from the breast. Metastasis detection is currently performed by pathologists reviewing large expanses of biological tissues. This process is labor intensive and error-prone. We present a framework to automatically detect and localize tumors as small as 100 x 100 pixels in gigapixel microscopy images sized 100,000 x 100,000 pixels. Our method leverages a convolutional neural network (CNN) architecture and obtains state-of-the-art results on the Camelyon16…
Technical University of Munich (TUM)（慕尼黑工业大学）；Klinikum Rechts der Isar（伊萨尔河畔大学医院）；Imperial College London（伦敦帝国理工学院）
In medical imaging, un-, semi-, or self-supervised pathology detection is often approached with anomaly- or out-of-distribution detection methods, whose inductive biases are not intentionally directed towards detecting pathologies, and are therefore sub-optimal for this task. To tackle this problem, we propose AutoSeg, an engine that can generate diverse artificial anomalies that resemble the properties of real-world pathologies. Our method can accurately segment unseen artificial anomalies and outperforms existing methods for pathology detection on a challenging real-world dataset of Chest X-…
Machine learning (ML) algorithms are optimized for the distribution represented by the training data. For outlier data, they often deliver predictions with equal confidence, even though these should not be trusted. In order to deploy ML-based digital pathology solutions in clinical practice, effective methods for detecting anomalous data are crucial to avoid incorrect decisions in the outlier scenario. We propose a new unsupervised learning approach for anomaly detection in histopathology data based on generative adversarial networks (GANs). Compared to the existing GAN-based methods that have…
Explaining Digital Pathology Models via Clustering Activations
Faculty of Informatics, Masaryk University, Brno, Czech Republic（马萨里克大学信息学院，布拉格，捷克共和国）；Institute of Computer Science, Masaryk University, Brno, Czech Republic（马萨里克大学计算机科学研究所，布拉格，捷克共和国）；Masaryk Memorial Cancer Institute, Brno, Czech Republic（马萨里克纪念癌症研究所，布拉格，捷克共和国）
Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs
University of Stavanger（斯塔万格大学）；University Medical Center Rotterdam（鹿特丹大学医学中心）；Erasmus MC Cancer Institute（伊拉斯姆斯大学医学中心癌症研究所）；Stavanger University Hospital（斯塔万格大学医院）；INCLIVA Biomedical Research Institute（INCLIVA生物医学研究所）；University of Valencia（瓦伦西亚大学）
Microsoft Research（微软研究院）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；University of Minnesota（明尼苏达大学）；Stanford University（斯坦福大学）
Deep learning techniques have emerged as a promising approach to highly accelerated MRI. However, recent reconstruction challenges have shown several drawbacks in current deep learning approaches, including the loss of fine image details even using models that perform well in terms of global quality metrics. In this study, we propose an end-to-end deep learning framework for image reconstruction and pathology detection, which enables a clinically aware evaluation of deep learning reconstruction quality. The solution is demonstrated for a use case in detecting meniscal tears on knee MRI studies…
Emory University（埃默里大学）；Indiana University Purdue University-Indianapolis（印第安纳大学与普渡大学印第安纳波利斯联合分校）
Deep learning approaches applied to medical imaging have reached near-human or better-than-human performance on many diagnostic tasks. For instance, the CheXpert competition on detecting pathologies in chest x-rays has shown excellent multi-class classification performance. However, training and validating deep learning models require extensive collections of images and still produce false inferences, as identified by a human-in-the-loop. In this paper, we introduce a practical approach to improve the predictions of a pre-trained model through Few-Shot Learning (FSL). After training and valida…
A robust and scalable framework for hallucination detection in virtual tissue staining and digital pathology
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；California NanoSystems Institute (CNSI)（加州纳米系统研究所）；Hadassah Hebrew University Medical Center（哈达萨希伯来大学医学中心）；University of Southern California（南加利福尼亚大学）
Imperial College London（帝国理工学院）；King’s College London（伦敦国王学院）；Friedrich–Alexander University Erlangen–Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）
Supervised learning of every possible pathology is unrealistic for many primary care applications like health screening. Image anomaly detection methods that learn normal appearance from only healthy data have shown promising results recently. We propose an alternative to image reconstruction-based and image embedding-based methods and propose a new self-supervised method to tackle pathological anomaly detection. Our approach originates in the foreign patch interpolation (FPI) strategy that has shown superior performance on brain MRI and abdominal CT data. We propose to use a better patch inte…
Deep Learning-Based Fixation Type Prediction for Quality Assurance in Digital Pathology
Institute of Pathology, Technical University of Munich, Germany（慕尼黑技术大学病理学研究所）；School of Computation, Information and Technology, Technical University of Munich, Germany（慕尼黑技术大学计算、信息与技术学院）；Munich Center for Machine Learning, Germany（慕尼黑机器学习中心）；Institute of Pathology, University of Regensburg, Regensburg, Germany（罗森堡大学病理学研究所）；Institute of Pathology and Molecular Diagnostics, University Hospital of Augsburg, Germany（埃格利希大学医院病理学与分子诊断研究所）；Digital Medicine, University Hospital of Augsburg, Germany（埃格利希大学医院数字医学）；Bavarian Cancer Research Center (BZKF), Augsburg, Germany（巴伐利亚癌症研究中心（BZKF））；Bio M {}^{\text{M}} Biotech Cluster Development GmbH, Martinsried, Germany（Bio M {}^{\text{M}} 生物技术集群发展 GmbH）；Institute of Pathology, Medical Faculty Mannheim, University Heidelberg, Germany（海德堡大学曼海姆医学学院病理学研究所）；Munich Data Science Institute, Technical University of Munich, Germany（慕尼黑数据科学研究所）
Islamic Azad University, Shabestar Branch（伊斯兰阿扎德大学沙贝斯塔尔分校）
There are different algorithms for vocal fold pathology diagnosis. These algorithms usually have three stages which are Feature Extraction, Feature Reduction and Classification. While the third stage implies a choice of a variety of machine learning methods, the first and second stages play a critical role in performance and accuracy of the classification system. In this paper we present initial study of feature extraction and feature reduction in the task of vocal fold pathology diagnosis. A new type of feature vector, based on wavelet packet decomposition and Mel-Frequency-Cepstral-Coefficie…
Multiplex Imaging Analysis in Pathology: a Comprehensive Review on Analytical Approaches and Digital Toolkits
Weill Cornell Medicine（威尔康奈尔医学院）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；University of Pisa（比萨大学）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Massachusetts Institute of Technology（麻省理工学院）；Broad Institute of MIT and Harvard（麻省理工学院和哈佛大学博德研究所）
Automatic segmentation of brain abnormalities is challenging, as they vary considerably from one pathology to another. Current methods are supervised and require numerous annotated images for each pathology, a strenuous task. To tackle anatomical variability, Unsupervised Anomaly Detection (UAD) methods are proposed, detecting anomalies as outliers of a healthy model learned using a Variational Autoencoder (VAE). Previous work on UAD adopted a 2D approach, meaning that MRIs are processed as a collection of independent slices. Yet, it does not fully exploit the spatial information contained in …
Digital pathology has attracted significant attention in recent years. Analysis of Whole Slide Images (WSIs) is challenging because they are very large, i.e., of Giga-pixel resolution. Identifying Regions of Interest (ROIs) is the first step for pathologists to analyse further the regions of diagnostic interest for cancer detection and other anomalies. In this paper, we investigate the use of RCNN, which is a deep machine learning technique, for detecting such ROIs only using a small number of labelled WSIs for training. For experimentation, we used real WSIs from a public hospital pathology s…
Deep learning is quickly becoming the leading methodology for medical image analysis. Given a large medical archive, where each image is associated with a diagnosis, efficient pathology detectors or classifiers can be trained with virtually no expert knowledge about the target pathologies. However, deep learning algorithms, including the popular ConvNets, are black boxes: little is known about the local patterns analyzed by ConvNets to make a decision at the image level. A solution is proposed in this paper to create heatmaps showing which pixels in images play a role in the image-level predic…
Burdur Mehmet Akif Ersoy University（布尔杜尔穆罕默德·阿基夫·埃尔索伊大学）；Süleyman Demirel University（苏莱曼·德米雷尔大学）
The aim of this study is to propose an alternative and hybrid solution method for diagnosing the disease from histopathology images taken from animals with paratuberculosis and intact intestine. In detail, the hybrid method is based on using both image processing and deep learning for better results. Reliable disease detection from histo-pathology images is known as an open problem in medical image processing and alternative solutions need to be developed. In this context, 520 histopathology images were collected in a joint study with Burdur Mehmet Akif Ersoy University, Faculty of Veterinary …
Although there have been several recent advances in the application of deep learning algorithms to chest x-ray interpretation, we identify three major challenges for the translation of chest x-ray algorithms to the clinical setting. We examine the performance of the top 10 performing models on the CheXpert challenge leaderboard on three tasks: (1) TB detection, (2) pathology detection on photos of chest x-rays, and (3) pathology detection on data from an external institution. First, we find that the top 10 chest x-ray models on the CheXpert competition achieve an average AUC of 0.851 on the ta…
Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）；Center for Behavioral Brain Sciences（行为脑科学研究中心）；German Center for Neurodegenerative Disease（德国神经退行性疾病研究中心）
Expert interpretation of anatomical images of the human brain is the central part of neuro-radiology. Several machine learning-based techniques have been proposed to assist in the analysis process. However, the ML models typically need to be trained to perform a specific task, e.g., brain tumour segmentation or classification. Not only do the corresponding training data require laborious manual annotations, but a wide variety of abnormalities can be present in a human brain MRI - even more than one simultaneously, which renders representation of all possible anomalies very challenging. Hence, …
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；University of Kansas Medical Center（堪萨斯大学医学中心）；UCLA David Geffen School of Medicine（加州大学洛杉矶分校大卫·格芬医学院）；Baylor College of Medicine（贝勒医学院）
This work presents a mitosis detection method with only one vanilla Convolutional Neural Network (CNN). Our method consists of two steps: given an image, we first apply a CNN using a sliding window technique to extract patches that have mitoses; we then calculate each extracted patch's class activation map to obtain the mitosis's precise location. To increase the model performance on high-domain-variance pathology images, we train the CNN with a data augmentation pipeline, a noise-tolerant loss that copes with unlabeled images, and a multi-rounded active learning strategy. In the MIDOG 2022 ch…
National and Kapodistrian University of Athens（雅典国立卡波迪斯特里亚大学）
Medical Informatics and the application of modern signal processing in the assistance of the diagnostic process in medical imaging is one of the more recent and active research areas today. This thesis addresses a variety of issues related to the general problem of medical image analysis, specifically in mammography, and presents a series of algorithms and design approaches for all the intermediate levels of a modern system for computer-aided diagnosis (CAD). The diagnostic problem is analyzed with a systematic approach, first defining the imaging characteristics and features that are relevant…
The Institute of Cancer Research（癌症研究所）；University College London Cancer Institute（伦敦大学学院癌症研究所）
Automated analysis of tissue sections allows a better understanding of disease biology and may reveal biomarkers that could guide prognosis or treatment selection. In digital pathology, less abundant cell types can be of biological significance, but their scarcity can result in biased and sub-optimal cell detection model. To minimize the effect of cell imbalance on cell detection, we proposed a deep learning pipeline that considers the abundance of cell types during model training. Cell weight images were generated, which assign larger weights to less abundant cells and used the weights to reg…
Scientific Computing and Imaging Institute（科学计算与成像研究所）；University of Utah（犹他大学）
Statistical shape modeling (SSM) directly from 3D medical images is an underutilized tool for detecting pathology, diagnosing disease, and conducting population-level morphology analysis. Deep learning frameworks have increased the feasibility of adopting SSM in medical practice by reducing the expert-driven manual and computational overhead in traditional SSM workflows. However, translating such frameworks to clinical practice requires calibrated uncertainty measures as neural networks can produce over-confident predictions that cannot be trusted in sensitive clinical decision-making. Existin…
Faculty of Engineering and Technology（工程与技术学院）；Faculty of Audiology and Speech Language Pathology（听觉与语言病理学学院）；Sri Ramachandra Institute of Higher Education and Research（Sri Ramachandra 高等教育与研究学院）
Medical Faculty Mannheim of Heidelberg University（海德堡大学曼海姆医学院）
Statistical analysis of magnetic resonance imaging (MRI) can help radiologists to detect pathologies that are otherwise likely to be missed. Deep learning (DL) has shown promise in modeling complex spatial data for brain anomaly detection. However, DL models have major deficiencies: they need large amounts of high-quality training data, are difficult to design and train and are sensitive to subtle changes in scanning protocols and hardware. Here, we show that also simple statistical methods such as voxel-wise (baseline and covariance) models and a linear projection method using spatial pattern…
Johns Hopkins University（约翰斯·霍普金斯大学）；University of Maryland（马里兰大学）
Ear related concerns and symptoms represents the leading indication for seeking pediatric healthcare attention. Despite the high incidence of such encounters, the diagnostic process of commonly encountered disease of the middle and external presents significant challenge. Much of this challenge stems from the lack of cost effective diagnostic testing, which necessitating the presence or absence of ear pathology to be determined clinically. Research has however demonstrated considerable variation among clinicians in their ability to accurately diagnose and consequently manage ear pathology. Wit…
Carnegie Mellon University（卡内基梅隆大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Petuum Inc.（Petuum 公司）
Due to the shortage of COVID-19 viral testing kits and the long waiting time, radiology imaging is used to complement the screening process and triage patients into different risk levels. Deep learning based methods have taken an active role in automatically detecting COVID-19 disease in chest x-ray images, as witnessed in many recent works in early 2020. Most of these works first train a Convolutional Neural Network (CNN) on an existing large-scale chest x-ray image dataset and then fine-tune it with a COVID-19 dataset at a much smaller scale. However, direct transfer across datasets from dif…
Domain-Specific Pre-training Improves Confidence in Whole Slide Image Classification
Birla Institute of Technology and Science, Pilani（毕拉理工与科学大学 Pilani校区）；Macquarie University（麦考瑞大学）；University of California, San Diego（加利福尼亚大学圣地亚哥分校）；TCS Research（塔塔咨询服务公司研究院）
Towards Global AI-Driven Cervical Cancer Screening
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University（海德堡大学）；National Center for Tumor Diseases (NCT) Heidelberg（海德堡国家肿瘤疾病中心）；Helmholtz Association（亥姆霍兹联合会）；University of Heidelberg（海德堡大学）；Medical Faculty Heidelberg（海德堡医学院）；University Hospital Heidelberg（海德堡大学医院）；German Consortium for Translational Cancer Research (DKTK)（德国转化癌症研究联盟）；National Center for Tumor Diseases (NCT) Dresden（德累斯顿国家肿瘤疾病中心）；University Hospital Carl Gustav Carus Dresden（德累斯顿卡尔·古斯塔夫·卡鲁斯大学医院）；Technische Universität Dresden（德累斯顿工业大学）；Helmholtz-Zentrum Dresden-Rossendorf (HZDR)（亥姆霍兹德累斯顿罗森多夫研究中心）；University of Bonn（波恩大学）；University Hospital Bonn（波恩大学医院）；University of Cologne（科隆大学）；University Hospital Cologne（科隆大学医院）；University of Duisburg-Essen（杜伊斯堡-埃森大学）；University Hospital Essen（埃森大学医院）；University of Freiburg（弗莱堡大学）；University Hospital Freiburg（弗莱堡大学医院）；University of Göttingen（哥廷根大学）；University Hospital Göttingen（哥廷根大学医院）；University of Hamburg（汉堡大学）；University Hospital Hamburg-Eppendorf（汉堡-埃彭多夫大学医院）；University of Jena（耶拿大学）；University Hospital Jena（耶拿大学医院）；University of Kiel（基尔大学）；University Hospital Schleswig-Holstein（石勒苏益格-荷尔斯泰因大学医院）；University of Leipzig（莱比锡大学）；University Hospital Leipzig（莱比锡大学医院）；University of Lübeck（吕贝克大学）；University Hospital Lübeck（吕贝克大学医院）；University of Magdeburg（马格德堡大学）；University Hospital Magdeburg（马格德堡大学医院）；University of Mainz（美因茨大学）；University Hospital Mainz（美因茨大学医院）；University of Marburg（马尔堡大学）；University Hospital Marburg（马尔堡大学医院）；University of Munich (LMU)（慕尼黑大学）；University Hospital Munich (LMU)（慕尼黑大学医院）；Technical University of Munich (TUM)（慕尼黑工业大学）；University Hospital rechts der Isar (TUM)（慕尼黑工业大学伊萨尔河右岸医院）；University of Münster（明斯特大学）；University Hospital Münster（明斯特大学医院）；University of Regensburg（雷根斯堡大学）；University Hospital Regensburg（雷根斯堡大学医院）；University of Rostock（罗斯托克大学）；University Hospital Rostock（罗斯托克大学医院）；University of Tübingen（蒂宾根大学）；University Hospital Tübingen（蒂宾根大学医院）；University of Ulm（乌尔姆大学）；University Hospital Ulm（乌尔姆大学医院）；University of Würzburg（维尔茨堡大学）；University Hospital Würzburg（维尔茨堡大学医院）
