A Comparative Study of Federated Learning Models for COVID-19 Detection

Deep learning is effective in diagnosing COVID-19 and requires a large amount of data to be effectively trained. Due to data and privacy regulations, hospitals generally have no access to data from other hospitals. Federated learning (FL) has been used to solve this problem, where it utilizes a distributed setting to train models in hospitals in a privacy-preserving manner. Deploying FL is not always feasible as it requires high computation and network communication resources. This paper evaluates five FL algorithms' performance and resource efficiency for Covid-19 detection. A decentralized setting with CNN networks is set up, and the performance of FL algorithms is compared with a centralized environment. We examined the algorithms with varying numbers of participants, federated rounds, and selection algorithms. Our results show that cyclic weight transfer can have better overall performance, and results are better with fewer participating hospitals. Our results demonstrate good performance for detecting COVID-19 patients and might be useful in deploying FL algorithms for covid-19 detection and medical image analysis in general

Fed-Safe: Securing Federated Learning in Healthcare Against Adversarial Attacks

This paper explores the security aspects of federated learning applications in medical image analysis. Current robustness-oriented methods like adversarial training, secure aggregation, and homomorphic encryption often risk privacy compromises. The central aim is to defend the network against potential privacy breaches while maintaining model robustness against adversarial manipulations. We show that incorporating distributed noise, grounded in the privacy guarantees in federated settings, enables the development of a adversarially robust model that also meets federated privacy standards. We conducted comprehensive evaluations across diverse attack scenarios, parameters, and use cases in cancer imaging, concentrating on pathology, meningioma, and glioma. The results reveal that the incorporation of distributed noise allows for the attainment of security levels comparable to those of conventional adversarial training while requiring fewer retraining samples to establish a robust model

The Hidden Adversarial Vulnerabilities of Medical Federated Learning

In this paper, we delve into the susceptibility of federated medical image analysis systems to adversarial attacks. Our analysis uncovers a novel exploitation avenue: using gradient information from prior global model updates, adversaries can enhance the efficiency and transferability of their attacks. Specifically, we demonstrate that single-step attacks (e.g. FGSM), when aptly initialized, can outperform the efficiency of their iterative counterparts but with reduced computational demand. Our findings underscore the need to revisit our understanding of AI security in federated healthcare settings

Imaging with extrinsic Raman labels

In two separate examples we demonstrate the use of extrinsic Raman scattering probes for imaging of biological samples. First, the distribution of cholesterol in a rat eye lens is determined with the use of the Raman scattered light from filipin, a molecule which binds specifically to cholesterol. The protein distribution in the same eye lens was obtained by using the 1450-cm-1 CH2 and CH3 bending modes as an intrinsic marker for protein. It appears that the cholesterol is concentrated in the membranes of the eye lens fibers, whereas the protein is distributed more evenly. Second, we demonstrate that phenotyping of lymphocytes can be done by using the Raman scattering of (antibody-coated) polystyrene spheres. The lymphocyte population was also fluorescently labeled with anti-CD4-FITC to demonstrate that Raman and fluorescence labeling can be used simultaneously. Finally, we discuss the potential advantages and disadvantages of using Raman labels

Identifying patients who may benefit from adaptive radiotherapy:Does the literature on anatomic and dosimetric changes in head and neck organs at risk during radiotherapy provide information to help?

AbstractIn the last decade, many efforts have been made to characterize anatomic changes of head and neck organs at risk (OARs) and the dosimetric consequences during radiotherapy. This review was undertaken to provide an overview of the magnitude and frequency of these effects, and to investigate whether we could find criteria to identify head and neck cancer patients who may benefit from adaptive radiotherapy (ART). Possible relationships between anatomic and dosimetric changes and outcome were explicitly considered. A literature search according to PRISMA guidelines was performed in MEDLINE and EMBASE for studies concerning anatomic or dosimetric changes of head and neck OARs during radiotherapy. Fifty-one eligible studies were found. The majority of papers reported on parotid gland (PG) anatomic and dosimetric changes. In some patients, PG mean dose differences between planning CT and repeat CT scans up to 10Gy were reported. In other studies, only minor dosimetric effects (i.e. <1Gy difference in PG mean dose) were observed as a result of significant anatomic changes. Only a few studies reported on the clinical relevance of anatomic and dosimetric changes in terms of complications or quality of life. Numerous potential selection criteria for anatomic and dosimetric changes during radiotherapy were found and listed. The heterogeneity between studies prevented unambiguous conclusions on how to identify patients who may benefit from ART in head and neck cancer. Potential pre-treatment selection criteria identified from this review include tumour location (nasopharyngeal carcinoma), age, body mass index, planned dose to the parotid glands, the initial parotid gland volume, and the overlap volume of the parotid glands with the target volume. These criteria should be further explored in well-designed and well-powered prospective studies, in which possible relationships between anatomic and dosimetric changes and outcome need to be established

The effects of computed tomography image characteristics and knot spacing on the spatial accuracy of B-spline deformable image registration in the head and neck geometry

Objectives: To explore the effects of computed tomography (CT) image characteristics and B-spline knot spacing (BKS) on the spatial accuracy of a B-spline deformable image registration (DIR) in the head-and-neck geometry. Methods: The effect of image feature content, image contrast, noise, and BKS on the spatial accuracy of a B-spline DIR was studied. Phantom images were created with varying feature content and varying contrast-to-noise ratio (CNR), and deformed using a known smooth B-spline deformation. Subsequently, the deformed images were repeatedly registered with the original images using different BKSs. The quality of the DIR was expressed as the mean residual displacement (MRD) between the known imposed deformation and the result of the B-spline DIR. Finally, for three patients, head-and-neck planning CT scans were deformed with a realistic deformation field derived from a rescan CT of the same patient, resulting in a simulated deformed image and an a-priori known deformation field. Hence, a B-spline DIR was performed between the simulated image and the planning CT at different BKSs. Similar to the phantom cases, the DIR accuracy was evaluated by means of MRD. Results: In total, 162 phantom registrations were performed with varying CNR and BKSs. MRD-values = +/- 250 HU and noise <+/- 200 HU. Decreasing the image feature content resulted in increased MRD-values at all BKSs. Using BKS = 15 mm for the three clinical cases resulted in an average MRD <1.0 mm. Conclusions: For synthetically generated phantoms and three real CT cases the highest DIR accuracy was obtained for a BKS between 10-20 mm. The accuracy decreased with decreasing image feature content, decreasing image contrast, and higher noise levels. Our results indicate that DIR accuracy in clinical CT images (typical noise levels <+/- 100 HU) will not be effected by the amount of image noise

Evaluation of a 3D surface imaging system for deep inspiration breath-hold patient positioning and intra-fraction monitoring

PURPOSE: To determine the accuracy of a surface guided radiotherapy (SGRT) system for positioning of breast cancer patients in breath-hold (BH) with respect to cone-beam computed tomography (CBCT). Secondly, to evaluate the thorax position stability during BHs with SGRT, when using an air-volume guidance system.METHODS AND MATERIALS: Eighteen left-sided breast cancer patients were monitored with SGRT during CBCT and treatment, both in BH. CBCT scans were matched on the target volume and the patient surface. The setup error differences were evaluated, including with linear regression analysis. The intra-fraction variability and stability of the air-volume guided BHs were determined from SGRT measurements. The variability was determined from the maximum difference between the different BH levels within one treatment fraction. The stability was determined from the difference between the start and end position of each BH.RESULTS: SGRT data correlated well with CBCT data. The correlation was stronger for surface-to-CBCT (0.61) than target volume-to-CBCT (0.44) matches. Systematic and random setup error differences were ≤ 2 mm in all directions. The 95% limits of agreement (mean ± 2SD) were 0.1 ± 3.0, 0.6 ± 4.1 and 0.4 ± 3.4 mm in the three orthogonal directions, for the surface-to-CBCT matches. For air-volume guided BHs, the variability detected with SGRT was 2.2, 2.8 and 2.3 mm, and the stability - 1.0, 2.1 and 1.5 mm, in three orthogonal directions. Furthermore, the SGRT system could detect unexpected patient movement, undetectable by the air-volume BH system.CONCLUSION: With SGRT, left-sided breast cancer patients can be positioned and monitored continuously to maintain position errors within 5 mm. Low intra-fraction variability and good stability can be achieved with the air-volume BH system, however, additional patient position information is available with SGRT, that cannot be detected with air-volume BH systems.</p

A novel semi auto-segmentation method for accurate dose and NTCP evaluation in adaptive head and neck radiotherapy

Background and purpose: Accurate segmentation of organs-at-risk (OARs) is crucial but tedious and time-consuming in adaptive radiotherapy (ART). The purpose of this work was to automate head and neck OAR-segmentation on repeat CT (rCT) by an optimal combination of human and auto-segmentation for accurate prediction of Normal Tissue Complication Probability (NTCP). Materials and methods: Human segmentation (HS) of 3 observers, deformable image registration (DIR) based contour propagation and deep learning contouring (DLC) were carried out to segment 15 OARs on 15 rCTs. The original treatment plan was re-calculated on rCT to obtain mean dose (D-mean) and con-sequent NTCP-predictions. The average Dmean and NTCP-predictions of the three observers were referred to as the gold standard to calculate the absolute difference of D-mean and NTCP-predictions (vertical bar AD(mean)vertical bar and vertical bar ANTCP vertical bar). Results: The average vertical bar AD(mean)vertical bar of parotid glands in HS was 1.40 Gy, lower than that obtained with DIR and DLC (3.64 Gy, p < 0.001 and 3.72 Gy, p < 0.001, respectively). DLC showed the highest vertical bar AD(mean)vertical bar in middle Pharyngeal Constrictor Muscle (PCM) (5.13 Gy, p = 0.01). DIR showed second highest vertical bar AD(mean)vertical bar in the cricopharyngeal inlet (2.85 Gy, p = 0.01). The semi auto-segmentation (SAS) adopted HS, DIR and DLC for segmentation of parotid glands, PCM and all other OARs, respectively. The 90th percentile vertical bar ANTCP vertical bar was 2.19%, 2.24%, 1.10% and 1.50% for DIR, DLC, HS and SAS respectively. Conclusions: Human segmentation of the parotid glands remains necessary for accurate interpretation of mean dose and NTCP during ART. Proposed semi auto-segmentation allows NTCP-predictions within 1.5% accuracy for 90% of the cases. (C) 2021 The Author(s). Published by Elsevier B.V

Inter-fraction motion robustness and organ sparing potential of proton therapy for cervical cancer

Purpose: Large-field photon radiotherapy is current standard in the treatment of cervical cancer patients. However, with the increasing availability of Pencil Beam Scanning Proton Therapy (PBS-PT) and robust treatment planning techniques, protons may have significant advantages for cervical cancer patients in the reduction of toxicity. In this study, PBS-PT and photon Volumetric Modulated Arc Therapy (VMAT) were compared, examining target coverage and organ at risk (OAR) dose, taking inter- and intra-fraction motion into account. Materials and methods: Twelve cervical cancer patients were included in this in-silico planning study. In all cases, a planning CT scan, five weekly repeat CT scans (reCTs) and an additional reCT 10 min after the first reCT were available. Two-arc VMAT and robustly optimised two- and four-field (2F and 4F) PBS-PT plans were robustly evaluated on planCTs and reCTs using set-up and range uncertainty. Nominal OAR doses and voxel-wise minimum target coverage robustness were compared. Results: Average voxel-wise minimum accumulated doses for pelvic target structures over all patients were adequate for both photon and proton treatment techniques (D98 > 95%, [91.7–99.3%]). Average accumulated dose of the para-aortic region was lower than the required 95%, D98 > 94.4% [91.1–98.2%]. With PBS-PT 4F, dose to all OARs was significantly lower than with VMAT. Major differences were observed for mean bowel bag V15Gy: 60% [39–70%] for VMAT vs 30% [10–52%] and 32% [9–54%] for PBS-PT 2F and 4F and for mean bone marrow V10Gy: 88% [82–97%] for VMAT vs 66% [60–73%] and 67% [60–75%] for PBS-PT 2F and 4F. Conclusion: Robustly optimised PBS-PT for cervical cancer patients shows equivalent target robustness against inter- and intra-fraction variability compared to VMAT, and offers significantly better OAR sparing

Comparison of computed tomography image features extracted by radiomics, self-supervised learning and end-to-end deep learning for outcome prediction of oropharyngeal cancer

Background and purpose: To compare the prediction performance of image features of computed tomography (CT) images extracted by radiomics, self-supervised learning and end-to-end deep learning for local control (LC), regional control (RC), locoregional control (LRC), distant metastasis-free survival (DMFS), tumor-specific survival (TSS), overall survival (OS) and disease-free survival (DFS) of oropharyngeal squamous cell carcinoma (OPSCC) patients after (chemo)radiotherapy.Methods and materials: The OPC-Radiomics dataset was used for model development and independent internal testing and the UMCG-OPC set for external testing. Image features were extracted from the Gross Tumor Volume contours of the primary tumor (GTVt) regions in CT scans when using radiomics or a self-supervised learning-based method (autoencoder). Clinical and combined (radiomics, autoencoder or end-to-end) models were built using multivariable Cox proportional-hazard analysis with clinical features only and both clinical and image features for LC, RC, LRC, DMFS, TSS, OS and DFS prediction, respectively.Results: In the internal test set, combined autoencoder models performed better than clinical models and combined radiomics models for LC, RC, LRC, DMFS, TSS and DFS prediction (largest improvements in C-index: 0.91 vs. 0.76 in RC and 0.74 vs. 0.60 in DMFS). In the external test set, combined radiomics models performed better than clinical and combined autoencoder models for all endpoints (largest improvements in LC, 0.82 vs. 0.71). Furthermore, combined models performed better in risk stratification than clinical models and showed good calibration for most endpoints.Conclusions: Image features extracted using self-supervised learning showed best internal prediction performance while radiomics features have better external generalizability.</p