Acquiring High Resolution (HR) Magnetic Resonance (MR) images requires the patient to remain still for long periods of time, which causes patient discomfort and increases the probability of motion induced image artifacts. A possible solution is to acquire low resolution (LR) images and to process them with the Super Resolution Generative Adversarial Network (SRGAN) to create a super-resolved version. This work applies SRGAN to MR images of the prostate and performs three experiments. The first experiment explores improving the in-plane MR image resolution by factors of 4 and 8, and shows that, while the PSNR and SSIM (Structural SIMilarity) metrics are lower than the isotropic bicubic interpolation baseline, the SRGAN is able to create images that have high edge fidelity. The second experiment explores anisotropic super-resolution via synthetic images, in that the input images to the network are anisotropically downsampled versions of HR images. This experiment demonstrates the ability of the modified SRGAN to perform anisotropic super-resolution, with quantitative image metrics that are comparable to those of the anisotropic bicubic interpolation baseline. Finally, the third experiment applies a modified version of the SRGAN to super-resolve anisotropic images obtained from the through-plane slices of the volumetric MR data. The output super-resolved images contain a significant amount of high frequency information that make them visually close to their HR counterparts. Overall, the promising results from each experiment show that super-resolution for MR images is a successful technique and that producing isotropic MR image volumes from anisotropic slices is an achievable goal.
Generative Adversarial Neural Networks (GANs) are applied to the synthetic generation of prostate lesion MRI images. GANs have been applied to a variety of natural images, is shown show that the same techniques can be used in the medical domain to create realistic looking synthetic lesion images. 16mm x 16mm patches are extracted from 330 MRI scans from the SPIE ProstateX Challenge 2016 and used to train a Deep Convolutional Generative Adversarial Neural Network (DCGAN) utilizing cutting edge techniques. Synthetic outputs are compared to real images and the implicit latent representations indu…
Whole gland (WG) segmentation of the prostate plays a crucial role in detection, staging and treatment planning of prostate cancer (PCa). Despite promise shown by deep learning (DL) methods, they rely on the availability of a considerable amount of annotated data. Augmentation techniques such as translation and rotation of images present an alternative to increase data availability. Nevertheless, the amount of information provided by the transformed data is limited due to the correlation between the generated data and the original. Based on the recent success of generative adversarial networks…
Influence of High-Performance Image-to-Image Translation Networks on Clinical Visual Assessment and Outcome Prediction: Utilizing Ultrasound to MRI Translation in Prostate Cancer
East China University of Science and Technology（华东理工大学）
Prostate cancer is one of the common diseases in men, and it is the most common malignant tumor in developed countries. Studies have shown that the male prostate incidence rate is as high as 2.5% to 16%, Currently, the inci-dence of prostate cancer in Asia is lower than that in the West, but it is increas-ing rapidly. If prostate cancer can be found as early as possible and treated in time, it will have a high survival rate. Therefore, it is of great significance for the diagnosis and treatment of prostate cancer. In this paper, we propose a trans-fer learning method based on deep neural netwo…
Istanbul Technical University（伊斯坦布尔理工大学）；Acibadem Mehmet Ali Aydinlar University School of Medicine（阿吉巴德姆·穆罕默德·阿里·艾丁拉尔大学医学院）；Bahcesehir University（巴耶塞特大学）
Biparametric MRI has emerged as an alternative to multiparametric prostate MRI, which eliminates the need for the potential harms to the patient due to the contrast medium. One major issue with biparametric MRI is difficulty to detect clinically significant prostate cancer (csPCA). Deep learning algorithms have emerged as an alternative solution to detect csPCA in cohort studies. We present a workflow which predicts csPCA on biparametric prostate MRI PI-CAI 2022 Challenge with over 10,000 carefully-curated prostate MRI exams. We propose to to segment the prostate gland first to the central gla…
University of California, Los Angeles (UCLA)（加利福尼亚大学洛杉矶分校）；The First Affiliated Hospital of China Medical University（中国医科大学附属第一医院）；VoxelCloud, Inc.（体素云公司）
Prostate cancer is the second leading cause of cancer death among men in the United States. The diagnosis of prostate MRI often relies on the accurate prostate zonal segmentation. However, state-of-the-art automatic segmentation methods often fail to produce well-contained volumetric segmentation of the prostate zones since certain slices of prostate MRI, such as base and apex slices, are harder to segment than other slices. This difficulty can be overcome by accounting for the cross-slice relationship of adjacent slices, but current methods do not fully learn and exploit such relationships. I…
Magnetic Resonance Imaging (MRI) is widely used for screening and staging prostate cancer. However, many prostate cancers have subtle features which are not easily identifiable on MRI, resulting in missed diagnoses and alarming variability in radiologist interpretation. Machine learning models have been developed in an effort to improve cancer identification, but current models localize cancer using MRI-derived features, while failing to consider the disease pathology characteristics observed on resected tissue. In this paper, we propose CorrSigNet, an automated two-step model that localizes p…
ProjectedEx: Enhancing Generation in Explainable AI for Prostate Cancer
Flinders University（弗林德斯大学）；The University of Adelaide（阿德莱德大学）；The Australian National University（澳大利亚国立大学）；The University of Sydney（悉尼大学）；Zhejiang University of Technology（浙江工业大学）；Guangdong Technion – Israel Institute of Technology（广东以色列理工学院）；University of Chinese Academy of Sciences（中国科学院大学）；Rice University（莱斯大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；The Pennsylvania State University（宾夕法尼亚州立大学）；La Trobe University（拉筹伯大学）
Anomaly-Driven Approach for Enhanced Prostate Cancer Segmentation
University of Amsterdam（阿姆斯特丹大学）；Department of Radiology, The Netherlands Cancer Institute（荷兰癌症研究所放射科）；GROW Research Institute for Oncology and Reproduction, Maastricht University（马斯特里赫特大学肿瘤学与生殖医学研究学院）
Mansoura University（曼苏拉大学）；Higher Institute of Engineering and Technology（高等工程技术学院）
Prostate cancer is the most dangerous cancer diagnosed in men worldwide. Prostate diagnosis has been affected by many factors, such as lesion complexity, observer visibility, and variability. Many techniques based on Magnetic Resonance Imaging (MRI) have been used for prostate cancer identification and classification in the last few decades. Developing these techniques is crucial and has a great medical effect because they improve the treatment benefits and the chance of patients' survival. A new technique that depends on MRI has been proposed to improve the diagnosis. This technique consists …
The Chinese University of Hong Kong（香港中文大学）；Stanford University（斯坦福大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Automated prostate segmentation in MRI is highly demanded for computer-assisted diagnosis. Recently, a variety of deep learning methods have achieved remarkable progress in this task, usually relying on large amounts of training data. Due to the nature of scarcity for medical images, it is important to effectively aggregate data from multiple sites for robust model training, to alleviate the insufficiency of single-site samples. However, the prostate MRIs from different sites present heterogeneity due to the differences in scanners and imaging protocols, raising challenges for effective ways o…
We hypothesize that probabilistic voxel-level classification of anatomy and malignancy in prostate MRI, although typically posed as near-identical segmentation tasks via U-Nets, require different loss functions for optimal performance due to inherent differences in their clinical objectives. We investigate distribution, region and boundary-based loss functions for both tasks across 200 patient exams from the publicly-available ProstateX dataset. For evaluation, we conduct a thorough comparative analysis of model predictions and calibration, measured with respect to multi-class volume segmentat…
Diffusion MRI preprocessing affects ADC estimation and automatic PI-RADS v2.1 classification in bi-parametric prostate MRI
Center for Image Sciences, University Medical Center Utrecht（乌得勒支大学医学中心影像科学中心）；Department of Radiotherapy, University Medical Center Utrecht（乌得勒支大学医学中心放射治疗科）；Cerebriu（Cerebriu公司）；Department of Diagnostic Radiology, Copenhagen University Hospital Rigshospitalet（哥本哈根大学医院 Rigshospitalet 诊断放射科）；Department of Diagnostic Radiology, Copenhagen University Hospital Herlev and Gentofte（哥本哈根大学医院海尔鲁夫与根措夫特医院诊断放射科）
Magnetic resonance imaging (MRI) has great potential to improve prostate cancer diagnosis. It can spare men with a normal exam from undergoing invasive biopsy while making biopsies more accurate in men with lesions suspicious for cancer. Yet, the subtle differences between cancer and confounding conditions, render the interpretation of MRI challenging. The tissue collected from patients that undergo pre-surgical MRI and radical prostatectomy provides a unique opportunity to correlate histopathology images of the entire prostate with MRI in order to accurately map the extent of prostate cancer …
The interpretation of prostate MRI suffers from low agreement across radiologists due to the subtle differences between cancer and normal tissue. Image registration addresses this issue by accurately mapping the ground-truth cancer labels from surgical histopathology images onto MRI. Cancer labels achieved by image registration can be used to improve radiologists' interpretation of MRI by training deep learning models for early detection of prostate cancer. A major limitation of current automated registration approaches is that they require manual prostate segmentations, which is a time-consum…
Purpose: We aimed to develop deep machine learning (DL) models to improve the detection and segmentation of intraprostatic lesions (IL) on bp-MRI by using whole amount prostatectomy specimen-based delineations. We also aimed to investigate whether transfer learning and self-training would improve results with small amount labelled data. Methods: 158 patients had suspicious lesions delineated on MRI based on bp-MRI, 64 patients had ILs delineated on MRI based on whole mount prostatectomy specimen sections, 40 patients were unlabelled. A non-local Mask R-CNN was proposed to improve the segmentat…
Tecnologico de Monterrey（蒙特雷科技大学）；CIATEQ A.C.（CIATEQ先进技术中心）；Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）；Hospital Sant Joan de Déu（圣胡安德迪奥斯医院）
Prostate cancer is the second-most frequently diagnosed cancer and the sixth leading cause of cancer death in males worldwide. The main problem that specialists face during the diagnosis of prostate cancer is the localization of Regions of Interest (ROI) containing a tumor tissue. Currently, the segmentation of this ROI in most cases is carried out manually by expert doctors, but the procedure is plagued with low detection rates (of about 27-44%) or overdiagnosis in some patients. Therefore, several research works have tackled the challenge of automatically segmenting and extracting features o…
The diagnosis of prostate cancer faces a problem with overdiagnosis that leads to damaging side effects due to unnecessary treatment. Research has shown that the use of multi-parametric magnetic resonance images to conduct biopsies can drastically help to mitigate the overdiagnosis, thus reducing the side effects on healthy patients. This study aims to investigate the use of deep learning techniques to explore computer-aid diagnosis based on MRI as input. Several diagnosis problems ranging from classification of lesions as being clinically significant or not to the detection and segmentation o…
Magnetic resonance imaging (MRI) is an increasingly important tool for the diagnosis and treatment of prostate cancer. However, interpretation of MRI suffers from high inter-observer variability across radiologists, thereby contributing to missed clinically significant cancers, overdiagnosed low-risk cancers, and frequent false positives. Interpretation of MRI could be greatly improved by providing radiologists with an answer key that clearly shows cancer locations on MRI. Registration of histopathology images from patients who had radical prostatectomy to pre-operative MRI allows such mapping…
University of California, San Francisco（加州大学旧金山分校）；Duke University（杜克大学）；University of Dusseldorf（杜塞尔多夫大学）；University of Washington（华盛顿大学）
Non-invasive prostate cancer detection from MRI has the potential to revolutionize patient care by providing early detection of clinically-significant disease (ISUP grade group >= 2), but has thus far shown limited positive predictive value. To address this, we present an MRI-based deep learning method for predicting clinically significant prostate cancer applicable to a patient population with subsequent ground truth biopsy results ranging from benign pathology to ISUP grade group~5. Specifically, we demonstrate that mixed supervision via diverse histopathological ground truth improves classi…
Prostate cancer was the third most common cancer in 2020 internationally, coming after breast cancer and lung cancer. Furthermore, in recent years prostate cancer has shown an increasing trend. According to clinical experience, if this problem is detected and treated early, there can be a high chance of survival for the patient. One task that helps diagnose prostate cancer is prostate segmentation from magnetic resonance imaging. Manual segmentation performed by clinical experts has its drawbacks such as: the high time and concentration required from observers; and inter- and intra-observer va…
Building robust deep learning-based models requires diverse training data, ideally from several sources. However, these datasets cannot be combined easily because of patient privacy concerns or regulatory hurdles, especially if medical data is involved. Federated learning (FL) is a way to train machine learning models without the need for centralized datasets. Each FL client trains on their local data while only sharing model parameters with a global server that aggregates the parameters from all clients. At the same time, each client's data can exhibit differences and inconsistencies due to t…
University of Stavanger（斯塔万格大学）；Stavanger University Hospital（斯塔万格大学医院）；University of Agder（阿格德尔大学）
Prostate cancer (PCa) is the second most common cancer diagnosed among men worldwide. The current PCa diagnostic pathway comes at the cost of substantial overdiagnosis, leading to unnecessary treatment and further testing. Bi-parametric magnetic resonance imaging (bp-MRI) based on apparent diffusion coefficient maps (ADC) and T2-weighted (T2w) sequences has been proposed as a triage test to differentiate between clinically significant (cS) and non-clinically significant (ncS) prostate lesions. However, analysis of the sequences relies on expertise, requires specialized training, and suffers fr…
With numerous medical tasks, the performance of deep models has recently experienced considerable improvements. These models are often adept learners. Yet, their intricate architectural design and high computational complexity make deploying them in clinical settings challenging, particularly with devices with limited resources. To deal with this issue, Knowledge Distillation (KD) has been proposed as a compression method and an acceleration technology. KD is an efficient learning strategy that can transfer knowledge from a burdensome model (i.e., teacher model) to a lightweight model (i.e., s…
Manchester Metropolitan University（曼彻斯特城市大学）；NHS（英国国民保健署）；The Leeds Teaching Hospital（利兹教学医院）；The Clatterbridge Cancer Center（克莱特布里奇癌症中心）；Leicester University（莱斯特大学）
Prostate radiotherapy is a well established curative oncology modality, which in future will use Magnetic Resonance Imaging (MRI)-based radiotherapy for daily adaptive radiotherapy target definition. However the time needed to delineate the prostate from MRI data accurately is a time consuming process. Deep learning has been identified as a potential new technology for the delivery of precision radiotherapy in prostate cancer, where accurate prostate segmentation helps in cancer detection and therapy. However, the trained models can be limited in their application to clinical setting due to di…
FastMRI Prostate: A Publicly Available, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging
New York University Grossman School of Medicine（纽约大学格罗斯曼医学院）；Siemens Medical Solutions USA（美国西门子医疗解决方案公司）；Courant Institute of Mathematical Sciences, New York University（纽约大学柯朗数学科学研究所）
To our knowledge, all deep computer-aided detection and diagnosis (CAD) systems for prostate cancer (PCa) detection consider bi-parametric magnetic resonance imaging (bp-MRI) only, including T2w and ADC sequences while excluding the 4D perfusion sequence,which is however part of standard clinical protocols for this diagnostic task. In this paper, we question strategies to integrate information from perfusion imaging in deep neural architectures. To do so, we evaluate several ways to encode the perfusion information in a U-Net like architecture, also considering early versus mid fusion strategi…
University of Magdeburg（马格德堡大学）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所 MEVIS）；Radboud University Medical Center（拉德堡德大学医学中心）；University Hospital Magdeburg（马格德堡大学医院）；University of Bremen（不来梅大学）
Background and Objective: Accurate and reliable segmentation of the prostate gland in MR images can support the clinical assessment of prostate cancer, as well as the planning and monitoring of focal and loco-regional therapeutic interventions. Despite the availability of multi-planar MR scans due to standardized protocols, the majority of segmentation approaches presented in the literature consider the axial scans only. Methods: We propose an anisotropic 3D multi-stream CNN architecture, which processes additional scan directions to produce a higher-resolution isotropic prostate segmentation.…
Mitigating 3D Prostate Biparametric MRI Data Scarcity through Domain Adaptation using Locally-Trained Latent Diffusion Models for Prostate Cancer Detection
Institute of Biomedical Engineering, University of Toronto（多伦多大学生物医学工程研究所）；Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital（圣心医院卢内尔-塔内本研究所）；KITE Research Institute, Toronto Rehabilitation Institute, University Health Network（多伦多康复研究所、KITE研究所在大学健康网络）；Joint Department of Medical Imaging, University of Toronto, Princess Margaret Hospital, and Sinai Health systems（多伦多大学联合医学影像部门、玛格丽特医院及辛纳医疗系统）；Department of Computer Science, University of Toronto（多伦多大学计算机科学系）；Faculty Affiliate of the Vector Institute, Toronto（向量研究所教职员工）
Univ Lyon（里昂大学）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；INSA Lyon（里昂国立应用科学学院）；UCBL（里昂第一大学）；CREATIS（里昂影像与信息处理研究中心）；Hospices Civils de Lyon（里昂民用医院集团）；Hôpital Edouard Herriot（爱德华·埃里奥医院）；University of Sherbrooke（舍布鲁克大学）
Multiparametric magnetic resonance imaging (mp-MRI) has shown excellent results in the detection of prostate cancer (PCa). However, characterizing prostate lesions aggressiveness in mp-MRI sequences is impossible in clinical practice, and biopsy remains the reference to determine the Gleason score (GS). In this work, we propose a novel end-to-end multi-class network that jointly segments the prostate gland and cancer lesions with GS group grading. After encoding the information on a latent space, the network is separated in two branches: 1) the first branch performs prostate segmentation 2) th…
In this work, we propose a deep U-Net based model to tackle the challenging task of prostate cancer segmentation by aggressiveness in MRI based on weak scribble annotations. This model extends the size constraint loss proposed by Kervadec et al. 1 in the context of multiclass detection and segmentation task. This model is of high clinical interest as it allows training on prostate biopsy samples and avoids time-consuming full annotation process. Performance is assessed on a private dataset (219 patients) where the full ground truth is available as well as on the ProstateX-2 challenge database,…
Leveraging Clinical Text and Class Conditioning for 3D Prostate MRI Generation
University of Toronto（多伦多大学）；Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital（西奈山医院卢嫩菲尔德-塔南鲍姆研究所）；KITE Research Institute, Toronto Rehabilitation Institute, University Health Network（大学健康网络多伦多康复研究所KITE研究所）；Vector Institute（Vector研究所）；Princess Margaret Hospital（玛格丽特公主医院）；Sinai Health System（西奈健康系统）
Non-Invasive Detection of PROState Cancer with Novel Time-Dependent Diffusion MRI and AI-Enhanced Quantitative Radiological Interpretation: PROS-TD-AI
University of Chile（智利大学）；Clínica Dávila（达维拉诊所）；Zhejiang University（浙江大学）；Hospital Clinic, Universitat de Barcelona（巴塞罗那大学医院诊所）；San Sebastián University（圣塞巴斯蒂安大学）
Facebook（脸书）；Stanford University School of Medicine（斯坦福大学医学院）；Stanford University（斯坦福大学）；VA Palo Alto Health Care System（帕洛阿尔托退伍军人医疗保健系统）
Prostate cancer is the second deadliest cancer for American men. While Magnetic Resonance Imaging (MRI) is increasingly used to guide targeted biopsies for prostate cancer diagnosis, its utility remains limited due to high rates of false positives and false negatives as well as low inter-reader agreements. Machine learning methods to detect and localize cancer on prostate MRI can help standardize radiologist interpretations. However, existing machine learning methods vary not only in model architecture, but also in the ground truth labeling strategies used for model training. In this study, we…
Deformable MRI Sequence Registration for AI-based Prostate Cancer Diagnosis
Radboudumc（拉德堡德大学医学中心）；Fraunhofer MEVIS（弗劳恩霍夫梅维斯研究所）；Universität zu Lübeck（吕贝克大学）；University Medical Center Groningen（格罗宁根大学医学中心）；Netherlands Cancer Institute（荷兰癌症研究所）；Norwegian University of Science and Technology（挪威科技大学）
A cross-modal generative model for incomplete and degraded prostate MRI with multicentre clinical validation
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属仁济医院）；National University of Singapore（新加坡国立大学）；Shanghai East Hospital, Tongji University School of Medicine（同济大学医学院附属东方医院）；McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston (UTHealth Houston)（德克萨斯大学休斯顿健康科学中心麦克威廉姆斯生物信息学院）；School of Transportation, Southeast University（东南大学交通学院）；Ningbo Hangzhou Bay Hospital（宁波杭州湾医院）；Changhai Hospital, Naval Medical University（海军军医大学长海医院）
DCE-FORMER: A Transformer-based Model With Mutual Information And Frequency-based Loss Functions For Early And Late Response Prediction In Prostate DCE-MRI
University of Toronto（多伦多大学）；Lunenfeld-Tanenbaum Research Institute, Sinai Health System（Sinai Health System 伦内菲尔德-塔嫩鲍姆研究所）；Brigham and Women’s Hospital（布莱根妇女医院）；The Hospital for Sick Children（SickKids 医院）
The segmentation of prostate whole gland and transition zone in Diffusion Weighted MRI (DWI) are the first step in designing computer-aided detection algorithms for prostate cancer. However, variations in MRI acquisition parameters and scanner manufacturing result in different appearances of prostate tissue in the images. Convolutional neural networks (CNNs) which have shown to be successful in various medical image analysis tasks including segmentation are typically sensitive to the variations in imaging parameters. This sensitivity leads to poor segmentation performance of CNNs trained on a …
UCL（伦敦大学学院）；UCLH NHS Foundation Trust（伦敦大学学院医院NHS基金会信托）
The need for training data can impede the adoption of novel imaging modalities for learning-based medical image analysis. Domain adaptation methods partially mitigate this problem by translating training data from a related source domain to a novel target domain, but typically assume that a one-to-one translation is possible. Our work addresses the challenge of adapting to a more informative target domain where multiple target samples can emerge from a single source sample. In particular we consider translating from mp-MRI to VERDICT, a richer MRI modality involving an optimized acquisition pr…
Leveraging multi-view data without annotations for prostate MRI segmentation: A contrastive approach
University of Stavanger（斯塔万格大学）；Karolinska Institute（卡罗林斯卡学院）；Capio S:t Göran Hospital（卡皮奥圣戈兰医院）；Danderyd Hospital（丹德吕德医院）；Stavanger University Hospital（斯塔万格大学医院）
Recent advances in medical imaging techniques have led to significant improvements in the management of prostate cancer (PCa). In particular, multi-parametric MRI (mp-MRI) continues to gain clinical acceptance as the preferred imaging technique for non-invasive detection and grading of PCa. However, the machine learning-based diagnosis systems for PCa are often constrained by the limited access to accurate lesion ground truth annotations for training. The performance of the machine learning system is highly dependable on both quality and quantity of lesion annotations associated with histopath…
Henry Ford Health System（亨利福特医疗系统）；Wayne State University（韦恩州立大学）
Deep learning models have had a great success in disease classifications using large data pools of skin cancer images or lung X-rays. However, data scarcity has been the roadblock of applying deep learning models directly on prostate multiparametric MRI (mpMRI). Although model interpretation has been heavily studied for natural images for the past few years, there has been a lack of interpretation of deep learning models trained on medical images. This work designs a customized workflow for the small and imbalanced data set of prostate mpMRI where features were extracted from a deep learning m…
Scaling Artificial Intelligence for Prostate Cancer Detection on MRI towards Organized Screening and Primary Diagnosis in a Global, Multiethnic Population (Study Protocol)
UCL（伦敦大学学院）；UCLH NHS Foundation Trust（伦敦大学学院医院国民保健服务基金会信托）
Any novel medical imaging modality that differs from previous protocols e.g. in the number of imaging channels, introduces a new domain that is heterogeneous from previous ones. This common medical imaging scenario is rarely considered in the domain adaptation literature, which handles shifts across domains of the same dimensionality. In our work we rely on stochastic generative modeling to translate across two heterogeneous domains at pixel space and introduce two new loss functions that promote semantic consistency. Firstly, we introduce a semantic cycle-consistency loss in the source domain…
