End-to-end weakly supervised semantic segmentation (E2E-WSSS) aims at optimizing a segmentation model in a single-stage training process based on only image annotations. Existing methods adopt an online-trained classification branch to provide pseudo annotations for supervising the segmentation branch. However, this strategy makes the classification branch dominate the whole concurrent training process, hindering these two branches from assisting each other. In our work, we treat these two branches equally by viewing them as diverse ways to generate the segmentation map, and add interactions on both their supervision and operation to achieve mutual promotion. For this purpose, a bidirectional supervision mechanism is elaborated to force the consistency between the outputs of these two branches. Thus, the segmentation branch can also give feedback to the classification branch to enhance the quality of localization seeds. Moreover, our method also designs interaction operations between these two branches to exchange their knowledge to assist each other. Experiments indicate our work outperforms existing end-to-end weakly supervised segmentation methods. Codes are available at https://github.com/zh460045050/BMP-WSSS.

Weakly supervised object localization (WSOL), adopting only image-level annotations to learn the pixel-level localization model, can release human resources in the annotation process. Most one-stage WSOL methods learn the localization model with multi-instance learning, making them only activate discriminative object parts rather than the whole object. In our work, we attribute this problem to the domain shift between the training and test process of WSOL and provide a novel perspective that views WSOL as a domain adaption (DA) task. Under this perspective, a DA-WSOL pipeline is elaborated to better assist WSOL with DA approaches by considering the specificities for the adaption of WSOL. Our DA-WSOL pipeline can discern the source-related and the Universum samples from other target samples based on a proposed target sampling strategy and then utilize them to solve the sample unbalancing and label unmatching between the source and target domain of WSOL. Experiments show that our pipeline outperforms SOTA methods on three WSOL benchmarks and can improve the performance of downstream weakly supervised semantic segmentation tasks.

Weakly supervised object localization (WSOL) relaxes the requirement of dense annotations for object localization by using image-level annotation to supervise the learning process. However, most WSOL methods only focus on forcing the object classifier to produce high activation score on object parts without considering the influence of background locations, causing excessive background activations and ill-pose background score searching. Based on this point, our work proposes a novel mechanism called the background-aware classification activation map (B-CAM) to add background awareness for WSOL training. Besides aggregating an object image-level feature for supervision, our B-CAM produces an additional background image-level feature to represent the pure-background sample. This additional feature can provide background cues for the object classifier to suppress the background activations on object localization maps. Moreover, our B-CAM also trained a background classifier with image-level annotation to produce adaptive background scores when determining the binary localization mask. Experiments indicate the effectiveness of the proposed B-CAM on four different types of WSOL benchmarks, including CUB-200, ILSVRC, OpenImages, and VOC2012 datasets.

Medical imaging denoising faces great challenges, yet is in great demand. With its distinctive characteristics, medical imaging denoising in the image domain requires innovative deep learning strategies. In this study, we propose a simple yet effective strategy, the content-noise complementary learning (CNCL) strategy, in which two deep learning predictors are used to learn the respective content and noise of the image dataset complementarily. A medical image denoising pipeline based on the CNCL strategy is presented, and is implemented as a generative adversarial network, where various representative networks (including U-Net, DnCNN, and SRDenseNet) are investigated as the predictors. The performance of these implemented models has been validated on medical imaging datasets including CT, MR, and PET. The results show that this strategy outperforms state-of-the-art denoising algorithms in terms of visual quality and quantitative metrics, and the strategy demonstrates a robust generalization capability. These findings validate that this simple yet effective strategy demonstrates promising potential for medical image denoising tasks, which could exert a clinical impact in the future. Code is available at: https://github.com/gengmufeng/CNCL-denoising.

Optical coherence tomography (OCT) is a widely-used modality in clinical imaging, which suffers from the speckle noise inevitably. Deep learning has proven its superior capability in OCT image denoising, while the difficulty of acquiring a large number of well-registered OCT image pairs limits the developments of paired learning methods. To solve this problem, some unpaired learning methods have been proposed, where the denoising networks can be trained with unpaired OCT data. However, majority of them are modified from the cycleGAN framework. These cycleGAN-based methods train at least two generators and two discriminators, while only one generator is needed for the inference. The dual-generator and dual-discriminator structures of cycleGAN-based methods demand a large amount of computing resource, which may be redundant for OCT denoising tasks. In this work, we propose a novel triplet cross-fusion learning (TCFL) strategy for unpaired OCT image denoising. The model complexity of our strategy is much lower than those of the cycleGAN-based methods. During training, the clean components and the noise components from the triplet of three unpaired images are cross-fused, helping the network extract more speckle noise information to improve the denoising accuracy. Furthermore, the TCFL-based network which is trained with triplets can deal with limited training data scenarios. The results demonstrate that the TCFL strategy outperforms state-of-the-art unpaired methods both qualitatively and quantitatively, and even achieves denoising performance comparable with paired methods. Code is available at: https://github.com/gengmufeng/TCFL-OCT.

Spectral computed tomography is able to provide quantitative information on the scanned object and enables material decomposition. Traditional projection-based material decomposition methods suffer from the nonlinearity of the imaging system, which limits the decomposition accuracy. Inspired by the generative adversarial network, we proposed a novel parallel multi-stream generative adversarial network (PMS-GAN) to perform projection-based multi-material decomposition in spectral computed tomography. By designing the differential map and incorporating the adversarial network into loss function, the decomposition accuracy was significantly improved with robust performance. The proposed network was quantitatively evaluated by both simulation and experimental study. The results show that PMS-GAN outperformed the reference methods with certain robustness. Compared with Pix2pix-GAN, PMS-GAN increased the structural similarity index by 172% on the contrast agent Ultravist370, 11% on bones, and 71% on bone marrow, respectively, in a simulated test scenario. In an experimental test scenario, 9% and 38% improvements of the structural similarity index on the biopsy needle and on a torso phantom were observed, respectively. The proposed network demonstrates its capability of multi-material decomposition and has certain potential toward clinical applications.

Optical coherence tomography angiography (OCTA) is a promising imaging modality for microvasculature studies. Deep learning networks have been widely applied in the field of OCTA reconstruction, benefiting from its powerful mapping capability among images. However, these existing deep learning-based methods depend on high-quality labels, which are hard to acquire considering imaging hardware limitations and practical data acquisition conditions. In this article, we proposed an unprecedented weakly supervised deep learning-based pipeline for OCTA reconstruction task, in the absence of high-quality training labels. The proposed pipeline was investigated on an in vivo animal dataset and a human eye dataset by a cross-validation strategy. Compared with supervised learning approaches, the proposed approach demonstrated similar or even better performance in the OCTA reconstruction task. These investigations indicate that the proposed weakly supervised learning strategy is well capable of performing OCTA reconstruction, and has a certain potential towards clinical applications.

PeopleN EURODEGENERATIVE diseases are progressive and difficult to diagnose in their early stages.Progressive impairment in the activities of daily living, as well as cognitive deterioration, leads to an increase in patient's dependence.Neuropsychiatric symptoms are common features and include psychosis (delusions and hallucinations), depressive mood, anxiety, irritability/lability, apathy, euphoria, disinhibition, agitation/aggression, aberrant motor activities, sleep disturbance, and eating disorders.Additionally, patients have a tendency to wander and misplace objects in their environment.Therefore, cognitive impaired patients need constant monitoring to ensure their wellbeing, and that they do not harm themselves.Ambient intelligence (AmI) is the computing paradigm that can provide support both for cognitive impaired people within their domestic environments and for clinicians to collect data needed to assess the disease.A novel class of applications can potentially emerge from the specialization of AmI technologies to the special domain of cognitive impaired users.However, such applications must be able to deal with peculiarities due to irrational and abnormal behaviors of cognitive impaired people within their environments, and their general attitude to not accepting wearable technologies.Several technical challenges remain to be solved before effective, intelligent, secure, and reliable AmI applications for the support of cognitive impaired people can be deployed trustfully into real environments.In this special section, the paper "The role of Virtual Motor Rehabilitation.A quantitative analysis between Acute and Chronic Patients with acquired brain injury," concerning acquired brain injury, compares the evolution of chronic patients with acute patients in a virtual rehabilitation program.For this study authors have developed a "Vestibular Virtual Rehabilitation" system.Results have shown a similar recovery for chronic and acute patients during the period of intervention.It has also been shown that chronic patients stop their improvement when they finish the training encouraging the migration of such rehabilitation technologies to home.In the contribution "An Adaptable and Flexible Framework for Assistive Living of Cognitively Impaired People" describes guidelines and an adaptable framework for the dynamic integration of assistive services with their related sensing technologies and interaction devices.The paper on "Statistical Anomaly Detection for Individuals with Cognitive Impairments" focuses on the identification of anomalies for users' routes, which may be useful to assist the impaired in case of losing one's way or to alert caregivers in case of wandering.The proposed approach relies on the exploitation Digital Object

The authors provide an overview of the present status of clinical and research applications for lasers in ophthalmology. They discuss therapeutic applications of lasers according to photothermal, photodisruptive, and photochemical mechanisms of laser-tissue interaction. Current diagnostic applications of lasers in ophthalmology are also reviewed. It is noted that lasers are in ubiquitous clinical use for many therapeutic and diagnostic purposes and their application has become the standard of care in the treatment of many eye diseases including diabetic retinopathy, vascular disease, glaucoma, and the treatment of capsular opacification following cataract surgery. Many new laser applications are under investigation, including refractive surgery, removal of cataracts, vitrea-retinal surgery, and diagnostic studies.>