Collaborative Training of Medical Artificial Intelligence Models with non-uniform Labels

Artificial intelligence (AI) methods are revolutionizing medical image analysis. However, robust AI models require large multi-site datasets for training. While multiple stakeholders have provided publicly available datasets, the ways in which these data are labeled differ widely. For example, one dataset of chest radiographs might contain labels denoting the presence of metastases in the lung, while another dataset of chest radiograph might focus on the presence of pneumonia. With conventional approaches, these data cannot be used together to train a single AI model. We propose a new framework that we call flexible federated learning (FFL) for collaborative training on such data. Using publicly available data of 695,000 chest radiographs from five institutions - each with differing labels - we demonstrate that large and heterogeneously labeled datasets can be used to train one big AI model with this framework. We find that models trained with FFL are superior to models that are trained on matching annotations only. This may pave the way for training of truly large-scale AI models that make efficient use of all existing data.Comment: 2 figures, 3 tables, 5 supplementary table

Fibroglandular Tissue Segmentation in Breast MRI using Vision Transformers -- A multi-institutional evaluation

Accurate and automatic segmentation of fibroglandular tissue in breast MRI screening is essential for the quantification of breast density and background parenchymal enhancement. In this retrospective study, we developed and evaluated a transformer-based neural network for breast segmentation (TraBS) in multi-institutional MRI data, and compared its performance to the well established convolutional neural network nnUNet. TraBS and nnUNet were trained and tested on 200 internal and 40 external breast MRI examinations using manual segmentations generated by experienced human readers. Segmentation performance was assessed in terms of the Dice score and the average symmetric surface distance. The Dice score for nnUNet was lower than for TraBS on the internal testset (0.909$\pm$0.069 versus 0.916$\pm$0.067, P<0.001) and on the external testset (0.824$\pm$0.144 versus 0.864$\pm$0.081, P=0.004). Moreover, the average symmetric surface distance was higher (=worse) for nnUNet than for TraBS on the internal (0.657$\pm$2.856 versus 0.548$\pm$2.195, P=0.001) and on the external testset (0.727$\pm$0.620 versus 0.584$\pm$0.413, P=0.03). Our study demonstrates that transformer-based networks improve the quality of fibroglandular tissue segmentation in breast MRI compared to convolutional-based models like nnUNet. These findings might help to enhance the accuracy of breast density and parenchymal enhancement quantification in breast MRI screening

Airway Management in COVID-19 as Aerosol Generating Procedure

2020 has seen the whole world battling a pandemic. Coronavirus Disease 2019 (COVID-19) is primarily transmitted through respiratory droplets when in close contact with an infected person, by direct contact, or by contact with contaminated objects and surfaces. Aerosol generating procedures (AGPs) like intubation have a high chance of generating large concentrations of infectious aerosols. AGPs potentially put healthcare workers at an increased risk of contracting the infection, and therefore special precautions are necessary during intubation. The procedure has to be performed by an expert operator who uses appropriate personal protective equipment (PPE). Modifications of known techniques have helped to reduce the chances of contracting the infection from patients. The use of checklists has become standard safe practice. This chapter looks at the current knowledge we have regarding this illness and how we should modify our practice to make managing the airway both safer for the patient and the healthcare workers involved. It addresses the preparation, staff protection, technical aspects and aftercare of patients who need airway intervention. It recommends simulation training to familiarize staff with modifications to routine airway management

Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology.

Artificial intelligence (AI) can extract visual information from histopathological slides and yield biological insight and clinical biomarkers. Whole slide images are cut into thousands of tiles and classification problems are often weakly-supervised: the ground truth is only known for the slide, not for every single tile. In classical weakly-supervised analysis pipelines, all tiles inherit the slide label while in multiple-instance learning (MIL), only bags of tiles inherit the label. However, it is still unclear how these widely used but markedly different approaches perform relative to each other. We implemented and systematically compared six methods in six clinically relevant end-to-end prediction tasks using data from N=2980 patients for training with rigorous external validation. We tested three classical weakly-supervised approaches with convolutional neural networks and vision transformers (ViT) and three MIL-based approaches with and without an additional attention module. Our results empirically demonstrate that histological tumor subtyping of renal cell carcinoma is an easy task in which all approaches achieve an area under the receiver operating curve (AUROC) of above 0.9. In contrast, we report significant performance differences for clinically relevant tasks of mutation prediction in colorectal, gastric, and bladder cancer. In these mutation prediction tasks, classical weakly-supervised workflows outperformed MIL-based weakly-supervised methods for mutation prediction, which is surprising given their simplicity. This shows that new end-to-end image analysis pipelines in computational pathology should be compared to classical weakly-supervised methods. Also, these findings motivate the development of new methods which combine the elegant assumptions of MIL with the empirically observed higher performance of classical weakly-supervised approaches. We make all source codes publicly available at https://github.com/KatherLab/HIA, allowing easy application of all methods to any similar task

Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge

The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field

Knowledge, beliefs, attitude, and practices of E-cigarette use among dental students: A multinational survey

E-cigarette use is a trend worldwide nowadays with mounting evidence on associated morbidities and mortality. Dentists can modify the smoking behaviors of their patients. This study aimed to explore the knowledge, beliefs, attitude, and practice of E-cigarette use among dental students. This multinational, cross-sectional, questionnaire-based study recruited undergraduate dental students from 20 dental schools in 11 countries. The outcome variable was current smoking status (non-smoker, E-cigarette user only, tobacco cigarette smoker only, dual user). The explanatory variables were country of residence, sex, age, marital status, and educational level. Multiple linear regression analysis was performed to explore the explanatory variables associated with E-cigarette smoking. Of the 5697 study participants, 5156 (90.8%) had heard about E-cigarette, and social media was the most reported source of information for 33.2% of the participants. For the 5676 current users of E-cigarette and/or tobacco smoking, 4.5% use E-cigarette, and 4.6% were dual users. There were significant associations between knowledge and country (P< 0.05), educational level (B = 0.12; 95% CI: 0.02, 0.21; P = 0.016) and smoking status (P< 0.05). The country of residence (P< 0.05) and smoking status (P< 0.05) were the only statistically significant factors associated with current smoking status. Similarly, there were statistically significant associations between attitude and country (P< 0.05 for one country only compared to the reference) and history of previous E-cigarette exposure (B = -0.52; 95% CI: -0.91, -0.13; P = 0.009). Also, the practice of E-cigarettes was significantly associated with country (P< 0.05 for two countries only compared to the reference) and gender (B = -0.33; 95% CI: -0.52, -0.13; P = 0.001). The knowledge of dental students about E-cigarette was unsatisfactory, yet their beliefs and attitudes were acceptable. Topics about E-cigarette should be implemented in the dental curriculum.Deanship of Scientific Research, King Saud University, for funding through the Vice Deanship of Scientific Research for Research Chairs. Qatar National Library for the open access funding

Oral health practices and self-reported adverse effects of E-cigarette use among dental students in 11 countries: an online survey

Objectives: E-cigarette use has become popular, particularly among the youth. Its use is associated with harmful general and oral health consequences. This survey aimed to assess self-reported oral hygiene practices, oral and general health events, and changes in physiological functions (including physical status, smell, taste, breathing, appetite, etc.) due to E-cigarette use among dental students. Methods: This online, multicounty survey involved undergraduate dental students from 20 dental schools across 11 different countries. The questionnaire included demographic characteristics, E-cigarette practices, self-reported complaints, and associated physiological changes due to E-cigarette smoking. Data were descriptively presented as frequencies and percentages. A Chi-square test was used to assess the potential associations between the study group and sub-groups with the different factors. Statistical analysis was performed using SPSS at P < 0.05. Results: Most respondents reported regular brushing of their teeth, whereas only 70% used additional oral hygiene aids. Reported frequencies of complaints ranged from as low as 3.3% for tongue inflammation to as high as 53.3% for headache, with significant differences between E-cigarette users and non-users. Compared to non-smokers, E-cigarette users reported significantly higher prevalence of dry mouth (33.1% vs. 23.4%; P < 0.001), black tongue (5.9% vs. 2.8%; P = 0.002), and heart palpitation (26.3%% vs. 22.8%; P = 0.001). Although two-thirds of the sample reported no change in their physiological functions, E-cigarette users reported significant improvement in their physiological functions compared to never smokers or tobacco users. Conclusion: Dental students showed good oral hygiene practices, but E-cigarette users showed a higher prevalence of health complications.Dental Biomaterials Research Chair, Deanship of Scientific Research, King Saud University. The funder has no role in the design of the study as well as in the methodology, analysis, and interpretation of the data

Transformers for CT Reconstruction From Monoplanar and Biplanar Radiographs

Computed Tomography (CT) scans provide detailed and accurate information of internal structures in the body. They are constructed by sending x-rays through the body from different directions and combining this information into a three-dimensional volume. Such volumes can then be used to diagnose a wide range of conditions and allow for volumetric measurements of organs. In this work, we tackle the problem of reconstructing CT images from biplanar x-rays only. X-rays are widely available and even if the CT reconstructed from these radiographs is not a replacement of a complete CT in the diagnostic setting, it might serve to spare the patients from radiation where a CT is only acquired for rough measurements such as determining organ size. We propose a novel method based on the transformer architecture, by framing the underlying task as a language translation problem. Radiographs and CT images are first embedded into latent quantized codebook vectors using two different autoencoder networks. We then train a GPT model, to reconstruct the codebook vectors of the CT image, conditioned on the codebook vectors of the x-rays and show that this approach leads to realistic looking images. To encourage further research in this direction, we make our code publicly available on GitHub: XXX

Cascaded Cross-Attention Networks for Data-Efficient Whole-Slide Image Classification Using Transformers

Whole-Slide Imaging allows for the capturing and digitization of high-resolution images of histological specimen. An automated analysis of such images using deep learning models is therefore of high demand. The transformer architecture has been proposed as a possible candidate for effectively leveraging the high-resolution information. Here, the whole-slide image is partitioned into smaller image patches and feature tokens are extracted from these image patches. However, while the conventional transformer allows for a simultaneous processing of a large set of input tokens, the computational demand scales quadratically with the number of input tokens and thus quadratically with the number of image patches. To address this problem we propose a novel cascaded cross-attention network (CCAN) based on the cross-attention mechanism that scales linearly with the number of extracted patches. Our experiments demonstrate that this architecture is at least on-par with and even outperforms other attention-based state-of-the-art methods on two public datasets: On the use-case of lung cancer (TCGA NSCLC) our model reaches a mean area under the receiver operating characteristic (AUC) of 0.970 $\pm$ 0.008 and on renal cancer (TCGA RCC) reaches a mean AUC of 0.985 $\pm$ 0.004. Furthermore, we show that our proposed model is efficient in low-data regimes, making it a promising approach for analyzing whole-slide images in resource-limited settings. To foster research in this direction, we make our code publicly available on GitHub: XXX