Multiple instance learning for eosinophil quantification of sinonasal histopathology images: A hierarchical determination on whole slide images.

KEY POINTS: We proposed a hierarchical framework including an unsupervised candidate image selection and a weakly supervised patch image detection based on multiple instance learning (MIL) to effectively estimate eosinophil quantities in tissue samples from whole slide images. MIL is an innovative approach that can help deal with the variability in cell distribution detection and enable automated eosinophil quantification from sinonasal histopathological images with a high degree of accuracy. The study lays the foundation for further research and development in the field of automated histopathological image analysis, and validation on more extensive and diverse datasets will contribute to real-world application.

ABC-Norm Regularization for Fine-Grained and Long-Tailed Image Classification.

Image classification for real-world applications often involves complicated data distributions such as fine-grained and long-tailed. To address the two challenging issues simultaneously, we propose a new regularization technique that yields an adversarial loss to strengthen the model learning. Specifically, for each training batch, we construct an adaptive batch prediction (ABP) matrix and establish its corresponding adaptive batch confusion norm (ABC-Norm). The ABP matrix is a composition of two parts, including an adaptive component to class-wise encode the imbalanced data distribution, and the other component to batch-wise assess the softmax predictions. The ABC-Norm leads to a norm-based regularization loss, which can be theoretically shown to be an upper bound for an objective function closely related to rank minimization. By coupling with the conventional cross-entropy loss, the ABC-Norm regularization could introduce adaptive classification confusion and thus trigger adversarial learning to improve the effectiveness of model learning. Different from most of state-of-the-art techniques in solving either fine-grained or long-tailed problems, our method is characterized with its simple and efficient design, and most distinctively, provides a unified solution. In the experiments, we compare ABC-Norm with relevant techniques and demonstrate its efficacy on several benchmark datasets, including (CUB-LT, iNaturalist2018); (CUB, CAR, AIR); and (ImageNet-LT), which respectively correspond to the real-world, fine-grained, and long-tailed scenarios.

Objective evaluation of biomaterial effects after injection laryngoplasty - Introduction of artificial intelligence-based ultrasonic image analysis.

OBJECTIVE: Hyaluronic acid (HA) can be degraded over time. However, persistence of the effects after injection laryngoplasty (IL) for unilateral vocal fold paralysis (UVFP), longer than expected from HA longevity, has been observed. The purpose of the study was to develop a methodology with clinical utility for objective evaluation of the temporal change in HA volume after IL using artificial intelligence (AI)-based ultrasonic assessment. DESIGN, SETTING AND PARTICIPANTS: Imaging phantoms simulating injected HA were built in different volumes for designing the algorithm for machine learning. Subsequently, five adult patients who had undergone IL with HA for UVFP were recruited for clinical evaluation. MAIN OUTCOME MEASURES: Estimated volumes were evaluated for injected HA by the automatic algorithm as well as voice outcomes at 2 weeks, and 2 and 6 months after IL. RESULTS: On imaging phantoms, contours on each frame were described well by the algorithm and the volume could be estimated accordingly. The error rates were 0%-9.2%. Moreover, the resultant contours of the HA area were captured in detail for all participants. The estimated volume decreased to an average of 65.76% remaining at 2 months and to a minimal amount at 6 months while glottal closure remained improved. CONCLUSION: The volume change of the injected HA over time for an individual was estimated non-invasively by AI-based ultrasonic image analysis. The prolonged effect after treatment, longer than HA longevity, was demonstrated objectively for the first time. The information is beneficial to achieve optimal cost-effectiveness of IL and improve the life quality of the patients.

Haze effect removal from image via haze density estimation in optical model.

Images/videos captured from optical devices are usually degraded by turbid media such as haze, smoke, fog, rain and snow. Haze is the most common problem in outdoor scenes because of the atmosphere conditions. This paper proposes a novel single image-based dehazing framework to remove haze artifacts from images, where we propose two novel image priors, called the pixel-based dark channel prior and the pixel-based bright channel prior. Based on the two priors with the haze optical model, we propose to estimate atmospheric light via haze density analysis. We can then estimate transmission map, followed by refining it via the bilateral filter. As a result, high-quality haze-free images can be recovered with lower computational complexity compared with the state-of-the-art approach based on patch-based dark channel prior.

Multimedia-assisted breathwalk-aware system.

Breathwalk is a science of combining specific patterns of footsteps synchronized with the breathing. In this study, we developed a multimedia-assisted Breathwalk-aware system which detects user's walking and breathing conditions and provides appropriate multimedia guidance on the smartphone. Through the mobile device, the system enhances user's awareness of walking and breathing behaviors. As an example application in slow technology, the system could help meditator beginners learn "walking meditation," a type of meditation which aims to be as slow as possible in taking pace, to synchronize footstep with breathing, and to land every footstep with toes first. In the pilot study, we developed a walking-aware system and evaluated whether multimedia-assisted mechanism is capable of enhancing beginner's walking awareness while walking meditation. Experimental results show that it could effectively assist beginners in slowing down the walking speed and decreasing incorrect footsteps. In the second experiment, we evaluated the Breathwalk-aware system to find a better feedback mechanism for learning the techniques of Breathwalk while walking meditation. The experimental results show that the visual-auditory mechanism is a better multimedia-assisted mechanism while walking meditation than visual mechanism and auditory mechanism.

Noncontact respiratory measurement of volume change using depth camera.

In this study, a system is developed to measure human chest wall motion for respiratory volume estimation without any physical contact. Based on depth image sensing technique, respiratory volume is estimated by measuring morphological changes of the chest wall. We evaluated the system and compared with a standard reference device, and the results show strong agreement in respiratory volume measurement [correlation coefficient: r=0.966]. The isovolume test presents small variations of the total respiratory volume during the isovolume maneuver (standard deviation<107 ml). Then, a regional pulmonary measurement test is evaluated by a patient, and the results show visibly difference of pulmonary functional between the diseased and the contralateral sides of the thorax after the thoracotomy. This study has big potential for personal health care and preventive medicine as it provides a novel, low-cost, and convenient way to measure user's respiration volume.