With promising results of machine learning based models in computer vision, applications on medical imaging data have been increasing exponentially. However, generalizations to complex real-world clinical data is a persistent problem. Deep learning models perform well when trained on standardized datasets from artificial settings, such as clinical trials. However, real-world data is different and translations are yielding varying results. The complexity of real-world applications in healthcare could emanate from a mixture of different data distributions across multiple device domains alongside the inevitable noise sourced from varying image resolutions, human errors, and the lack of manual gradings. In addition, healthcare applications not only suffer from the scarcity of labeled data, but also face limited access to unlabeled data due to HIPAA regulations, patient privacy, ambiguity in data ownership, and challenges in collecting data from different sources. These limitations pose additional challenges to applying deep learning algorithms in healthcare and clinical translations. In this paper, we utilize self-supervised representation learning methods, formulated effectively in transfer learning settings, to address limited data availability. Our experiments verify the importance of diverse real-world data for generalization to clinical settings. We show that by employing a self-supervised approach with transfer learning on a multi-domain real-world dataset, we can achieve 16% relative improvement on a standardized dataset over supervised baselines.
Histopathology images are gigapixel-sized and include features and information at different resolutions. Collecting annotations in histopathology requires highly specialized pathologists, making it expensive and time-consuming. Self-training can alleviate annotation constraints by learning from both labeled and unlabeled data, reducing the amount of annotations required from pathologists. We study the design of teacher-student self-training systems for Non-alcoholic Steatohepatitis (NASH) using clinical histopathology datasets with limited annotations. We evaluate the models on in-distribution…
Recent advances in training deep learning models have demonstrated the potential to provide accurate chest X-ray interpretation and increase access to radiology expertise. However, poor generalization due to data distribution shifts in clinical settings is a key barrier to implementation. In this study, we measured the diagnostic performance for 8 different chest X-ray models when applied to (1) smartphone photos of chest X-rays and (2) external datasets without any finetuning. All models were developed by different groups and submitted to the CheXpert challenge, and re-applied to test dataset…
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…
Testing of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound
Technical University of Denmark（技术大学）；Pioneer Centre for AI（先锋人工智能中心）；University of Copenhagen（哥本哈根大学）；Slagelse Hospital（斯拉格尔塞医院）；CAMES Rigshospitalet（CAMES皇家医院）
Evaluating the Generalization of Neuroimaging Foundation Models on African Brain MRI
Shenyang Medical College（沈阳医学院）；University of Ibadan（伊巴丹大学）；University of Ghana（加纳大学）；Obafemi Awolowo University（奥巴费米·阿沃洛沃大学）；Kwame Nkrumah University of Science and Technology（夸梅·恩克鲁玛科技大学）
Medical world models in healthcare: foundations, applications, and challenges for trustworthy clinical translation
National and Local Joint Engineering Laboratory of Computer Aided Design, School of Software Engineering, Dalian University（大连大学软件工程学院计算机辅助设计国家地方联合工程实验室）；Department of Radiology, Xinhua Hospital Affiliated to Dalian University（大连大学附属新华医院放射科）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；University of California, San Francisco（加州大学旧金山分校）；Yale University（耶鲁大学）；The Hong Kong Polytechnic University（香港理工大学）
Medical World Model: Generative Simulation of Tumor Evolution for Treatment Planning
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Johns Hopkins University（约翰霍普金斯大学）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）；University of California, San Francisco（加州大学旧金山分校）
本文提出医学世界模型 MeWM，结合视觉-语言策略模型与肿瘤生成动力学模型模拟治疗后肿瘤演化，并通过逆动力学模型优化个体化治疗方案，将最优 TACE 方案选择的 F1 分数提升 13%。
Generalization is one of the key challenges in the clinical validation and application of deep learning models to medical images. Studies have shown that such models trained on publicly available datasets often do not work well on real-world clinical data due to the differences in patient population and image device configurations. Also, manually annotating clinical images is expensive. In this work, we propose an unsupervised domain adaptation (UDA) method using Cycle-GAN to improve the generalization ability of the model without using any additional manual annotations.
Current face detection algorithms are extremely generalized and can obtain decent accuracy when detecting the adult faces. These approaches are insufficient when handling outlier cases, for example when trying to detect the face of a neonate infant whose face composition and expressions are relatively different than that of the adult. It is furthermore difficult when applied to detect faces in a complicated setting such as the Neonate Intensive Care Unit. By training a state-of-the-art face detection model, You-Only-Look-Once, on a proprietary dataset containing labelled neonate faces in a cli…
Universitat de Barcelona（巴塞罗那大学）；Hospital de la Santa Creu i Sant Pau（圣十字圣保罗医院）；Universitat Autònoma de Barcelona（巴塞罗那自治大学）；Hospital Universitari Vall d’Hebron（瓦尔德希伯伦大学医院）
The domain generalization problem has been widely investigated in deep learning for non-contrast imaging over the last years, but it received limited attention for contrast-enhanced imaging. However, there are marked differences in contrast imaging protocols across clinical centers, in particular in the time between contrast injection and image acquisition, while access to multi-center contrast-enhanced image data is limited compared to available datasets for non-contrast imaging. This calls for new tools for generalizing single-domain, single-center deep learning models across new unseen doma…
Multi-View Stenosis Classification Leveraging Transformer-Based Multiple-Instance Learning Using Real-World Clinical Data
Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital（人工智能在医疗与健康中的chair，慕尼黑技术大学及慕尼黑技术大学医院）；Department of Computing, Imperial College London, UK, and Munich Center for Machine Learning (MCML)（计算系，伦敦帝国学院，英国，以及慕尼黑机器学习中心）；Department of Internal Medicine, TUM University Hospital（内科系，慕尼黑技术大学医院）
Data from clinical real-world settings is characterized by variability in quality, machine-type, setting, and source. One of the primary goals of medical computer vision is to develop and validate artificial intelligence (AI) based algorithms on real-world data enabling clinical translations. However, despite the exponential growth in AI based applications in healthcare, specifically in ophthalmology, translations to clinical settings remain challenging. Limited access to adequate and diverse real-world data inhibits the development and validation of translatable algorithms. In this paper, we …
Explainable Multimodal Deep Learning Integrating Imaging and Clinical Data for Oral Potentially Malignant Disorder Detection
Wyant College of Optical Sciences, University of Arizona（亚利桑那大学怀恩特光学科学学院）；Beckman Laser Institute & Medical Clinic, University of California Irvine（加州大学尔湾分校贝克曼激光研究所与医学诊所）
State of Abdominal CT Datasets: A Critical Review of Bias, Clinical Relevance, and Real-world Applicability
Data Science and Machine Learning Lab (DML), Department of Computer Engineering, Sharif University of Technology（数据科学与机器学习实验室（DML），计算机工程系，谢里夫大学）；Data-Driven and Digital Health (D3M), The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai（数据驱动与数字健康（D3M），查尔斯·布朗弗曼个性化医学研究所，辛辛那提医学院）；Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医学科学大学）；Tehran University of Medical Sciences Cancer Research Institute（德黑兰医学院医学科学癌症研究所）
Assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears
MyC；APHP Paris, La Pitié-Salpêtrière, Sorbonne Université（APHP巴黎，拉皮蒂埃-萨尔佩特里耶，索邦大学）；CHU of Montpellier, University of Montpellier（蒙彼利埃大学附属医院，蒙彼利埃大学）；CHU Campus, Ministère de la Santé et de l'hygiène Publique（CHU校区，卫生与公共卫生部）；CHU Sylvanus Olympio, Lomé, Togo（索维拉尼奥利奥医院，洛美，多哥）；Faculté des Sciences de la Santé, Université de Lomé（健康科学学院，洛美大学）
