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Conference Name: 2022 30th Signal Processing and Communications Applications Conference (SIU) Date of Conference: 15-18 May 2022 The capacity of magnetic resonance imaging (MRI) to capture several contrasts within a session enables it to obtain increased diagnostic information. However, such multi-contrast MRI tests take a long time to scan, resulting in acquiring just a part of the essential contrasts. Synthetic multi-contrast MRI has the potential to improve radiological observations and consequent image analysis activities. Because of its ability to generate realistic results, generative adversarial networks (GAN) have recently been the most popular choice for medical imaging synthesis. This paper proposes a novel generative adversarial framework to improve the image synthesis quality in multi-contrast MRI. Our method uses transfer learning to adapt pre-trained autoencoder networks to the synthesis task and enhances the image synthesis quality by initializing the training process with more optimal network parameters. We demonstrate that the proposed method outperforms competing synthesis models by 0.95 dB on average on a well-known multi-contrast MRI dataset.

Conference Name: 2022 30th Signal Processing and Communications Applications Conference (SIU) Date of Conference: 15-18 May 2022 Magnetic resonance imaging (MRI) is used in many diagnostic applications as it has a high soft-tissue contrast and is a non-invasive medical imaging method. MR signal levels differs according to the parameters T1, T2 and PD that change with respect to the chemical structure of the tissues. However, long scan times might limit acquiring images from multiple contrasts or if the multi-contrasts images are acquired, the contrasts are noisy. To overcome this limitation of MRI, multi-contrast synthesis can be utilized. In this paper, we propose a deep learning method based on Channel-Exchanging-Network (CEN) for multi-contrast image synthesis. Demonstrations are provided on IXI dataset. The proposed model based on CEN is compared against alternative methods based on CNNs and GANs. Our results show that the proposed model achieves superior performance to the competing methods.

Conference Name: 2022 30th Signal Processing and Communications Applications Conference (SIU) Date of Conference: 15-18 May 2022 Magnetic Resonance Imaging (MRI) is an imaging technique used to produce detailed anatomical images. Acquiring multiple contrast MRI images requires long scan times forcing the patient to remain still. Scan times can be reduced by synthesising unacquired contrasts from acquired contrasts. In recent years, deep generative adversarial networks have been used to synthesise contrasts using one-to-one mapping. Deeper networks can solve more complex functions, however, their performance can decline due to problems such as overfitting and vanishing gradients. In this study, we propose adding skip connections to generative models to overcome the decline in performance with increasing complexity. This will allow the network to bypass unnecessary parameters in the model. Our results show an increase in performance in one-to-one image synthesis by integrating skip connections.

Auscultation is an established technique in clinical assessment of symptoms for respiratory disorders. Auscultation is safe and inexpensive, but requires expertise to diagnose a disease using a stethoscope during hospital or office visits. However, some clinical scenarios require continuous monitoring and automated analysis of respiratory sounds to pre-screen and monitor diseases, such as the rapidly spreading COVID-19. Recent studies suggest that audio recordings of bodily sounds captured by mobile devices might carry features helpful to distinguish patients with COVID-19 from healthy controls. Here, we propose a novel deep learning technique to automatically detect COVID-19 patients based on brief audio recordings of their cough and breathing sounds. The proposed technique first extracts spectrogram features of respiratory recordings, and then classifies disease state via a hierarchical vision transformer architecture. Demonstrations are provided on a crowdsourced database of respiratory sounds from COVID-19 patients and healthy controls. The proposed transformer model is compared against alternative methods based on state-of-the-art convolutional and transformer architectures, as well as traditional machine-learning classifiers. Our results indicate that the proposed model achieves on par or superior performance to competing methods. In particular, the proposed technique can distinguish COVID-19 patients from healthy subjects with over 94% AUC.

Learning-based translation between MRI contrasts involves supervised deep models trained using high-quality source- and target-contrast images derived from fully-sampled acquisitions, which might be difficult to collect under limitations on scan costs or time. To facilitate curation of training sets, here we introduce the first semi-supervised model for MRI contrast translation (ssGAN) that can be trained directly using undersampled k-space data. To enable semi-supervised learning on undersampled data, ssGAN introduces novel multi-coil losses in image, k-space, and adversarial domains. The multi-coil losses are selectively enforced on acquired k-space samples unlike traditional losses in single-coil synthesis models. Comprehensive experiments on retrospectively undersampled multi-contrast brain MRI datasets are provided. Our results demonstrate that ssGAN yields on par performance to a supervised model, while outperforming single-coil models trained on coil-combined magnitude images. It also outperforms cascaded reconstruction-synthesis models where a supervised synthesis model is trained following self-supervised reconstruction of undersampled data. Thus, ssGAN holds great promise to improve the feasibility of learning-based multi-contrast MRI synthesis.

Conference Name: 3rd MICCAI Workshop on Distributed, Collaborative, and Federated Learning, DeCaF 2022, and the 2nd MICCAI Workshop on Affordable AI and Healthcare, FAIR 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022 Date of Conference: 22 September 2022 MRI translation models learn a mapping from an acquired source contrast to an unavailable target contrast. Collaboration between institutes is essential to train translation models that can generalize across diverse datasets. That said, aggregating all imaging data and training a centralized model poses privacy problems. Recently, federated learning (FL) has emerged as a collaboration framework that enables decentralized training to avoid sharing of imaging data. However, FL-trained translation models can deteriorate by the inherent heterogeneity in the distribution of MRI data. To improve reliability against domain shifts, here we introduce a novel specificity-preserving FL method for MRI contrast translation. The proposed approach is based on an adversarial model that adaptively normalizes the feature maps across the generator based on site-specific latent variables. Comprehensive FL experiments were conducted on multi-site datasets to show the effectiveness of the proposed approach against prior federated methods in MRI contrast translation.

Conference Name: 2022 30th Signal Processing and Communications Applications Conference (SIU) Date of Conference: 15-18 May 2022 Exploration is maintained in continuous control using undirected methods, in which random noise perturbs the network parameters or selected actions. Exploration that is intrinsically driven is a good alternative to undirected techniques. However, it is only studied for discrete action domains. The intrinsic incentives in the existing reinforcement learning literature are unified together in this study by a deterministic artificial goal generation rule for off-policy learning. The agent gains additional reward through this practice if it chooses actions that lead it to useful state spaces. An extensive set of experiments indicates that the introduced artificial reward rule significantly improves the performance of the off-policy baseline algorithms. Keşif, rastgele gürültünün ağ parametrelerini veya seçilen eylemleri bozduğu, yönlendirilmemiş yöntemler kullanılarak sürekli kontrolde sürdürülmektedir. İçsel olarak yönlendirilen keşif, yönlendirilmemiş tekniklere iyi bir alternatiftir ancak yalnızca ayrık eylem alanları için incelenmiştir. Mevcut pekiştirmeli öğrenme literatüründeki içsel teşvikler, bu çalışmada politika-dışı öğrenme için deterministik bir yapay hedef oluşturma kuralıyla birleştirilmiştir. Ajan, kendisini yararlı durum uzaylarına götüren eylemleri seçerse, bu uygulama aracılığıyla ek bir ödül kazanmaktadır. Kapsamlı bir deney seti, tanıtılan yapay ödül kuralının, politika-dışı temel algoritmaların performansını önemli ölçüde geliştirdiğini göstermektedir.

Conference Name: 2022 30th Signal Processing and Communications Applications Conference (SIU) Date of Conference: 15-18 May 2022 Magnetic Resonance Imaging (MRI) is the favored modality in multi-modal medical imaging due to its safety and ability to acquire various different contrasts of the anatomy. Availability of multiple contrasts accumulates diagnostic information and, therefore, can improve radiological observations. In some scenarios, acquiring all contrasts might be challenging due to reluctant patients and increased costs associated with additional scans. That said, synthetically obtaining missing MRI pulse sequences from the acquired sequences might prove to be useful for further analyses. Recently introduced Generative Adversarial Network (GAN) models offer state-of-the-art performance in learning MRI synthesis. However, the proposed generative approaches learn a distinct model for each conditional contrast to contrast mapping. Learning a distinct synthesis model for each individual task increases the time and memory demands due to the increased number of parameters and training time. To mitigate this issue, we propose a novel unified synthesis model, bottleneck sharing GAN (bsGAN), to consolidate learning of synthesis tasks in multi-contrast MRI. bsGAN comprises distinct convolutional encoders and decoders for each contrast to increase synthesis performance. A central information bottleneck is employed to distill hidden representations. The bottleneck, based on residual convolutional layers, is shared across contrasts to avoid introducing many learnable parameters. Qualitative and quantitative comparisons on a multi-contrast brain MRI dataset show the effectiveness of the proposed method against existing unified synthesis methods.

Conference Name: 2022 30th Signal Processing and Communications Applications Conference (SIU) Date of Conference: 15-18 May 2022 The Batch-Constrained Q-learning algorithm is shown to overcome the extrapolation error and enable deep reinforcement learning agents to learn from a previously collected fixed batch of transitions. However, due to conditional Variational Autoencoders (VAE) used in the data generation module, the BCQ algorithm optimizes a lower variational bound and hence, it is not generalizable to environments with large state and action spaces. In this paper, we show that the performance of the BCQ algorithm can be further improved with the employment of one of the recent advances in deep learning, Generative Adversarial Networks. Our extensive set of experiments shows that the introduced approach significantly improves BCQ in all of the control tasks tested. Moreover, the introduced approach demonstrates robust generalizability to environments with large state and action spaces in the OpenAI Gym control suite. Toplu-Kısıtlı Q-öğrenme (TKQ) algoritmasının, ekstrapolasyon hatasının üstesinden geldiği ve derin pekiştirmeli öğrenme ajanlarının önceden toplanmış sabit bir deneyim kümesinden öğrenebildiği gösterilmiştir. Ancak, veri oluşturma modülünde kullanılan Koşullu Değişken Özkodlayıcılar (KDÖ) sebebiyle TKQ algoritması daha düşük bir değişken sınırını optimize etmektedir ve bu nedenle, büyük durum ve eylem uzaylarına sahip ortamlara genelleştirilememektedir. Bu bildiride, derin öğrenmedeki son gelişmelerden biri olan Üretken Çekişmeli Ağlar’ın (ÜÇA) kullanılmasıyla TKQ algoritmasının performansının daha da geliştirilebileceği gösterilmektedir. Kapsamlı deneyler, tanıtılan yaklaşımın test edilen her bir kontrol görevinde TKQ’yu önemli ölçüde geliştirdiğini göstermektedir. Ayrıca tanıtılan yaklaşım, OpenAI Gym kontrol setindeki geniş durum ve eylem boşluklarına sahip ortamlara hızlı bir genelleştirilebilirlik sergilemektedir.

Conference Name: International Conf on Electrical and Electronic Engineering (ICEEE) Date of conference: 29-31 March 2022 Magnetic resonance imaging (MRI) is the preferred modality among radiologists in the clinic due to its superior depiction of tissue contrast. Its ability to capture different contrasts within an exam session allows it to collect additional diagnostic information. However, such multi-contrast MRI exams take a long time to scan, resulting in acquiring just a portion of the required contrasts. Consequently, synthetic multi-contrast MRI can improve subsequent radiological observations and image analysis tasks like segmentation and detection. Because of this significant potential, multi-contrast MRI synthesis approaches are gaining popularity. Recently, generative adversarial networks (GAN) have become the de facto choice for synthesis tasks in medical imaging due to their sensitivity to realism and high-frequency structures. In this study, we present a novel generative adversarial approach for multi-contrast MRI synthesis that combines the learning of deep residual convolutional networks and spatial modulation introduced by an attention gating mechanism to synthesize high-quality MR images. We show the superiority of the proposed approach against various synthesis models on multi-contrast MRI datasets.

Generative adversarial models with convolutional neural network (CNN) backbones have recently been established as state-of-the-art in numerous medical image synthesis tasks. However, CNNs are designed to perform local processing with compact filters, and this inductive bias compromises learning of contextual features. Here, we propose a novel generative adversarial approach for medical image synthesis, ResViT, that leverages the contextual sensitivity of vision transformers along with the precision of convolution operators and realism of adversarial learning.} ResViT's generator employs a central bottleneck comprising novel aggregated residual transformer (ART) blocks that synergistically combine residual convolutional and transformer modules. Residual connections in ART blocks promote diversity in captured representations, while a channel compression module distills task-relevant information. A weight sharing strategy is introduced among ART blocks to mitigate computational burden. A unified implementation is introduced to avoid the need to rebuild separate synthesis models for varying source-target modality configurations. Comprehensive demonstrations are performed for synthesizing missing sequences in multi-contrast MRI, and CT images from MRI. Our results indicate superiority of ResViT against competing CNN- and transformer-based methods in terms of qualitative observations and quantitative metrics.