Several studies underscore the potential of deep learning in identifying complex patterns, leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse datasets, ...required for training, is a significant challenge in medicine and can rarely be found in individual institutions. Multi-institutional collaborations based on centrally-shared patient data face privacy and ownership challenges. Federated learning is a novel paradigm for data-private multi-institutional collaborations, where model-learning leverages all available data without sharing data between institutions, by distributing the model-training to the data-owners and aggregating their results. We show that federated learning among 10 institutions results in models reaching 99% of the model quality achieved with centralized data, and evaluate generalizability on data from institutions outside the federation. We further investigate the effects of data distribution across collaborating institutions on model quality and learning patterns, indicating that increased access to data through data private multi-institutional collaborations can benefit model quality more than the errors introduced by the collaborative method. Finally, we compare with other collaborative-learning approaches demonstrating the superiority of federated learning, and discuss practical implementation considerations. Clinical adoption of federated learning is expected to lead to models trained on datasets of unprecedented size, hence have a catalytic impact towards precision/personalized medicine.
De-centralized data analysis becomes an increasingly preferred option in the healthcare domain, as it alleviates the need for sharing primary patient data across collaborating institutions. This ...highlights the need for consistent harmonized data curation, pre-processing, and identification of regions of interest based on uniform criteria.
Towards this end, this manuscript describes the
derated
umor
egmentation (FeTS) tool, in terms of software architecture and functionality.
The primary aim of the FeTS tool is to facilitate this harmonized processing and the generation of gold standard reference labels for tumor sub-compartments on brain magnetic resonance imaging, and further enable federated training of a tumor sub-compartment delineation model across numerous sites distributed across the globe, without the need to share patient data.
Building upon existing open-source tools such as the Insight Toolkit and Qt, the FeTS tool is designed to enable training deep learning models targeting tumor delineation in either centralized or federated settings. The target audience of the FeTS tool is primarily the computational researcher interested in developing federated learning models, and interested in joining a global federation towards this effort. The tool is open sourced athttps://github.com/FETS-AI/Front-End.
Convolutional neural network (CNN) models obtain state of the art performance on image classification, localization, and segmentation tasks. Limitations in computer hardware, most notably memory size ...in deep learning accelerator cards, prevent relatively large images, such as those from medical and satellite imaging, from being processed as a whole in their original resolution. A fully convolutional topology, such as U-Net, is typically trained on down-sampled images and inferred on images of their original size and resolution, by simply dividing the larger image into smaller (typically overlapping) tiles, making predictions on these tiles, and stitching them back together as the prediction for the whole image. In this study, we show that this tiling technique combined with translationally-invariant nature of CNNs causes small, but relevant differences during inference that can be detrimental in the performance of the model. Here we quantify these variations in both medical (i.e., BraTS) and non-medical (i.e., satellite) images and show that training a 2D U-Net model on the whole image substantially improves the overall model performance. Finally, we compare 2D and 3D semantic segmentation models to show that providing CNN models with a wider context of the image in all three dimensions leads to more accurate and consistent predictions. Our results suggest that tiling the input to CNN models-while perhaps necessary to overcome the memory limitations in computer hardware-may lead to undesirable and unpredictable errors in the model's output that can only be adequately mitigated by increasing the input of the model to the largest possible tile size.
A motor illusion was created to separate human subjects' perception of arm movement from their actual movement during figure drawing. Trajectories constructed from cortical activity recorded in ...monkeys performing the same task showed that the actual movement was represented in the primary motor cortex, whereas the visualized, presumably perceived, trajectories were found in the ventral premotor cortex. Perception and action representations can be differentially recognized in the brain and may be contained in separate structures.
Telementoring in robotic surgery Santomauro, Michael; Reina, G Anthony; Stroup, Sean P ...
Current opinion in urology
23, Številka:
2
Journal Article
To provide an overview of the current concepts regarding telementoring with robotic surgery highlighting recent advances with respect to urological minimally invasive surgery (MIS).
As robotic ...surgery continues to evolve, telementoring will become a viable alternative to traditional on-site surgical proctoring.
MIS represents one of the most important breakthroughs in medicine over the past few decades. Newcomers to MIS need the guidance of more experienced, 'high volume' mentors to achieve the superior outcomes promised by MIS over conventional techniques.Telementoring, a subset of telemedicine, allows a surgeon at a remote site to offer intraoperative guidance via telecommunication networks. MIS lends itself well to telementoring techniques for several reasons; the primary surgeon performing MIS is working off of video images of the surgical field or images sent to a console. As such, the mentor is seeing the exact same images as the primary surgeon. In this review, we highlight many of the latest technologies in telemedicine, which are applicable to MIS and provide an overview of the pitfalls, which need to be overcome to make telementoring (and eventually telesurgery) a standard tool in the MIS arsenal.
Single-unit activity in area M1 was recorded in awake, behaving monkeys during a three-dimensional (3D) reaching task performed in a virtual reality environment. This study compares motor cortical ...discharge rate to both the hand's velocity and the arm's joint angular velocities. Hand velocity is considered a parameter of extrinsic space because it is measured in the Cartesian coordinate system of the monkey's workspace. Joint angular velocity is considered a parameter of intrinsic space because it is measured relative to adjacent arm/body segments. In the initial analysis, velocity was measured as the difference in hand position or joint posture between the beginning and ending of the reach. Cortical discharge rate was taken as the mean activity between these two times. This discharge rate was compared through a regression analysis to either an extrinsic-coordinate model based on the three components of hand velocity or to an intrinsic-coordinate model based on seven joint angular velocities. The model showed that velocities about four degrees-of-freedom (elbow flexion/extension, shoulder flexion/extension, shoulder internal/external rotation, and shoulder adduction/abduction) were those best represented in the sampled population of recorded activity. Patterns of activity recorded across the cortical population at each point in time throughout the task were used in a second analysis to predict the temporal profiles of joint angular velocity and hand velocity. The population of cortical units from area M1 matched the hand velocity and three of the four major joint angular velocities. However, shoulder adduction/abduction could not be predicted even though individual cells showed good correlation to movement on this axis. This was also the only major degree-of-freedom not well correlated to hand velocity, suggesting that the other apparent relations between joint angular velocity and neuronal activity may be due to intrinsic-extrinsic correlations inherent in reaching movements.
Abstract
BACKGROUND
Application of deep learning to neuro-oncology has shown promising clinically relevant results for tumor classification, localization, and segmentation. Hardware limitations, ...typically memory size of graphics cards, prevent magnetic resonance imaging (MRI) volumes from being processed as a whole, and hence they are divided into smaller, overlapping tiles. Deep learning algorithms (e.g., U-Net) can then be trained and applied for predictions on such tiles, followed by their combination/stitching as the final prediction for the whole volume. We investigate the hypothesis that image tiling options, such as tile placing, size, overlap, and stitching, introduce variations with adverse effects on predictions, both in terms of inconsistency and accuracy.
METHODS
We utilized the publicly available BraTS 2018 dataset of 285 baseline pre-operative MRI glioma scans, with corresponding expert tumor boundary annotations. We implemented a 3D U-Net to predict boundaries of the whole tumor extent, by virtue of the abnormal hyper-intense signal of T2-FLAIR scans.
RESULTS
Simply flipping the tile horizontally, or translating it by one voxel, produces different predictions. Use of small tiles (64x64x64 voxels) yields substantially more false positive predictions than when using larger tile size (i.e., 128x128x128 voxels). Overlapping tiles produce conflicting predictions, leading to ambiguous interpretations upon their stitching. In areas of overlapping tiles, rounding followed by averaging the overlapping predictions produce superior results to the inverse sequence. All these are particularly noticeable in the margins of the abnormal signal and in areas of large contrast variation.
CONCLUSIONS
Although tiling is a workaround for hardware limitations, it introduces variations detrimental to accuracy. Tiling of neuro-oncology scans for computational analysis using deep learning leads to non-generalizable, non-reproducible results, thereby affecting the performance and potential clinical translatability of such algorithms. Careful considerations and standardization recommendations should be established and appropriately documented for performing such analyses, in order to avoid misinterpretation of results.
Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, ...reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform.Federated learning can be used to train medical AI models on sensitive personal data while preserving important privacy properties; however, the sensitive nature of the data makes it difficult to evaluate approaches reproducibly on real data. The MedPerf project presented by Karargyris et al. provides the tools and infrastructure to distribute models to healthcare facilities, such that they can be trained and evaluated in realistic settings.
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