Shuhang Wang

Schepens Eye Research Institute

Abstract Post-operative urinary retention is a medical condition where patients cannot urinate despite having a full bladder. Ultrasound imaging of the bladder is used to estimate urine volume for early diagnosis and management of urine retention. Moreover, the use of bladder ultrasound can reduce the need for an indwelling urinary catheter and the...

Post-operative urinary retention is a medical condition where patients cannot urinate despite having a full bladder. Ultrasound imaging of the bladder is intermittently used to estimate urine volume for early diagnosis and management of urine retention. Moreover, the use of bladder ultrasound can reduce the need for an indwelling urinary catheter a...

Antral follicle Count (AFC) is a non-invasive biomarker used to assess ovarian reserves through transvaginal ultrasound (TVUS) imaging. Antral follicles’ diameter is usually in the range of 2–10 mm. The primary aim of ovarian reserve monitoring is to measure the size of ovarian follicles and the number of antral follicles. Manual follicle measureme...

Medical image segmentation plays a crucial role in diagnosing and staging diseases. It facilitates image analysis and quantification in multiple applications, but building the right appropriate solutions is essential and highly reliant on the features of different datasets and computational resources. Most existing approaches provide segmentation f...

Deep learning has been widely utilized for medical image segmentation. The most commonly used U-Net and its variants often share two common characteristics but lack solid evidence for the effectiveness. First, each block (i.e., consecutive convolutions of feature maps of the same resolution) outputs feature maps from the last convolution, limiting...

Steatosis is the most common cause of chronic liver disease worldwide. Ultrasound helps diagnose steatosis due to its wide availability, non-invasiveness, and safety. The hepatorenal index (HRI) is a clinically used biomarker for diagnosing steatosis. To study the automated calculation of HRI from ultrasound images, we identified a retrospective co...

Conventional transfer learning leverages weights of pre-trained networks, but mandates the need for similar neural architectures. Alternatively, knowledge distillation can transfer knowledge between heterogeneous networks but often requires access to the original training data or additional generative networks. Knowledge transfer between networks c...

Purpose: Visually impaired people may be allowed to drive if they wear bioptic telescopes. Bioptic driving safety is debatable, especially given that the telescopes are seldom used by most bioptic drivers. This preliminary study examined bioptic safety based on critical events that occurred in naturalistic daily driving. Methods: Daily driving a...

haze images

This paper proposes a novel U-Net variant using stacked dilated convolutions for medical image segmentation (SDU-Net). SDU-Net adopts the architecture of vanilla U-Net with modifications in the encoder and decoder operations (an operation indicates all the processing for feature maps of the same resolution). Unlike vanilla U-Net which incorporates...

Modern deep learning algorithms geared towards clinical adaption rely on a significant amount of high fidelity labeled data. Low-resource settings pose challenges like acquiring high fidelity data and becomes the bottleneck for developing artificial intelligence applications. Ultrasound images, stored in Digital Imaging and Communication in Medicin...

Purpose: The purpose of this study was to investigate the telescope use behaviors in natural daily driving of people with reduced visual acuity licensed to drive with a bioptic (a small spectacle-mounted telescope). Methods: A large dataset (477 hours) of naturalistic driving was collected from 19 bioptic drivers (visual acuity 20/60 to 20/160 w...

Gaining environmental awareness through lateral head scanning (yaw rotations) is important for driving safety, especially when approaching intersections. Therefore, head scanning movements could be an important behavioral metric for driving safety research and driving risk mitigation systems. Tracking head scanning movements with a single in-car ca...

Background Controlled naturalistic driving for examining impacts of cognitive impairment on driving safety is rare. Objective Evaluating the safety among drivers with mild cognitive impairment based on near collision incidents using naturalistic driving, and investigating its correlation with cognitive measures. Methods Frequency of near collisio...

We observed an attribute entanglement phenomenon: samples with similar attributes but from different classes can easily result in recognition errors. This problem is an important cause that results in recognition errors. To address this problem, we propose a new loss function, namely the entanglement loss. It penalizes the compactness between the m...

Images captured under uneven lighting conditions often suffer from low visual quality in dark areas. It is hard to make a good balance between improving detail visibility and suppressing over-enhancement. To cope with this, we propose a method to enhance local contrast in dark regions. Inspired by reinterpreting the Weber contrast, we treat an imag...

Visual context is fundamental to understand human actions in videos. However, the discriminative temporal information of videos is usually sparse and most frames are redundant mixed with a large amount of interference information, which may result in redundant computation and recognition failure. Hence, an important question is how to efficiently e...

In this paper, an effective image fusion method for infrared image and visible image is proposed for generating a high-quality fused image to deal with the issue that existing image fusion methods suffer from loss of tiny details. The major contributions are as follows: (1) We apply the Co-occurrence filter (CoF), a recently proposed edge-preservin...

Analyzing naturalistic driving behavior recorded with in-car cameras is an ecologically valid method for measuring driving errors, but it is time intensive and not easily applied on a large scale. This study validated a semi-automated, computerized method using archival naturalistic driving data collected for drivers with mild Alzheimer’s disease (...

Lane changes are important behaviors to study in driving research. Automated detection of lane-change events is required to address the need for data reduction of a vast amount of naturalistic driving videos. This paper presents a method to deal with weak lane-marker patterns as small as a couple of pixels wide. The proposed method is novel in its...

The full-text paper and demo executable for "perceptually motivated image enhancement method for non uniformly illuminated images".

Non-uniformly illuminated images often suffer from low visibility in dark areas. Traditional methods usually enhance non-uniformly illuminated images by bringing out the details in the dark areas, but easily result in over-enhancement. Motivated by the Weber contrast model, we proposes a perceptually inspired image enhancement method, which treats...

Gaze-contingent displays have been widely used in vision research and virtual reality applications. Due to data transmission, image processing, and display preparation, the time delay between the eye tracker and the monitor update may lead to a misalignment between the eye position and the image manipulation during eye movements. We propose a metho...

Enhancement of non-uniformly illuminated images often suffers from over-enhancement and produces unnatural results. This paper presents a naturalness preserved enhancement method for non-uniformly illuminated images, using a priori multi-layer lightness statistics acquired from high-quality images. Our work makes three important contributions: desi...

Image haze removal has been extensively studied, but there has been no such an image database regarding the haze level. It is not convenient for readers to verify the assumptions or priors that are supposed to be useful for haze removal, and meanwhile, it is not fair to compare the performance of haze removal methods, which are effective for images...

Wireless sensor networks are required in smart applications to provide accurate control, where the high density of sensors brings in a large quantity of redundant data. In order to reduce the waste of limited network resources, data aggregation is utilized to avoid redundancy forwarding. However, most of aggregation schemes reduce information accur...

Outdoor images captured in bad-weather conditions usually have poor intensity contrast and color saturation since the light arriving at the camera is severely scattered or attenuated. The task of improving image quality in poor conditions remains a challenge. Existing methods of image quality improvement are usually effective for a small group of i...

Outdoor images captured in bad-weather conditions usually have poor intensity contrast and color saturation since the light arriving at the camera is severely scattered or attenuated. The task of improving image quality in poor conditions remains a challenge. Existing methods of image quality improvement are usually effective for a small group of i...

Twenty-seven observers performed intensive visual search tasks with curved and flat monitors. Based on a 22-item eyestrain questionnaire, fewer subjects reported eyestrain, difficulty-to-focus and blurred-vision symptoms with a curved monitor than with a flat monitor. Fewer subjects had reduced saccade peak speed following the curved monitor use.

As a challenging problem, image enhancement plays an important role in computer vision applications and has been widely studied. As one of the most difficult issues of image enhancement, outdoor nighttime image enhancement suffers from noise amplification easily. To solve this problem, this study proposes a parameter-adaptive nighttime image enhanc...

Dehazing is an important but difficult issue for image processing. Recently, many dehazing algorithms have been proposed based on the dark channel prior. However, these algorithms fail to achieve a good tradeoff between the dehazing performance and the computational complexity. Moreover, the perceptual quality of these algorithms can be further imp...

As a challenging problem, image haze removal plays an important role in computer vision applications. The dark channel prior has been widely studied for haze removal since it is simple and effective; however, it still suffers from oversaturation, artefacts and dark-look. To resolve these problems, this study proposes a method of single image haze r...

Image enhancement plays an important role in image processing and analysis. Among various enhancement algorithms, Retinex-based algorithms can efficiently enhance details and have been widely adopted. Since Retinex-based algorithms regard illumination removal as a default preference and fail to limit the range of reflectance, the naturalness of non...

In this paper, an efficient image enhancement method based on Retinex and lightness decomposition is proposed, which enhances details and preserves the naturalness simultaneously. The quality of an enhanced image is determined by two factors, details and naturalness. Accordingly, the lightness is proposed to be decomposed into reflex lightness and...

In computer vision, the existing model used for dehazing is based on single scattering of atmosphere, so it cannot eliminate blurring in the images. In this paper, we propose a novel model for dehazing by including multiple scattering into the existing model. Based on visual manifestations, we conclude that scene radiance can be blurred by neighbor...