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Ultra-widefield (UWF) retinal imaging stands as a pivotal modality for detecting major eye diseases such as diabetic retinopathy and retinal detachment. However, UWF exhibits a well-documented limitation in terms of low resolution and artifacts in the macular area, thereby constraining its clinical diagnostic accuracy, particularly for macular diseases like age-related macular degeneration. Conventional supervised super-resolution techniques aim to address this limitation by enhancing the resolution of the macular region through the utilization of meticulously paired and aligned fundus image ground truths. However, obtaining such refined paired ground truths is a formidable challenge. To tackle this issue, we propose an unpaired, degradation-aware, super-resolution technique for enhancing UWF retinal images. Our approach leverages recent advancements in deep learning: specifically, by employing generative adversarial networks and attention mechanisms. Notably, our method excels at enhancing and super-resolving UWF images without relying on paired, clean ground truths. Through extensive experimentation and evaluation, we demonstrate that our approach not only produces visually pleasing results but also establishes state-of-the-art performance in enhancing and super-resolving UWF retinal images. We anticipate that our method will contribute to improving the accuracy of clinical assessments and treatments, ultimately leading to better patient outcomes.

The task of 3D human-body reconstruction (3DHR), which mostly utilizes parametric pose and shape representations, has witnessed significant advances in recent years. However, the application of 3DHR techniques in handling real-world in-the-wild data, still faces limitations. Training the 3DHR model in such scenario with 3D human pose’s ground truth (GT) is non-trivial. Curating the accurate 3D human pose GT for in-the-wild scenes remains difficult due to various factors. Recent test-time refinement approaches on 3DHR task leverage 2D off-the-shelf human keypoints information to support the lack of 3D supervision on in-the-wild data. However, we observed that additional 2D supervision alone could cause overfitting issue on common 3DHR backbones, making the 3DHR test-time refinement task seem intractable. We answer this challenge by proposing a strategy that complements 3DHR test-time refinement work under a collaborative approach. Specifically, we initially apply a pre-adaptation approach that works by collaborating various 3DHR models in a single framework to directly improve their initial outputs. This approach is then further combined with the test-time adaptation work under specific settings that minimize the overfitting issue to further boost the 3DHR performance. The whole framework is termed as 3DHR-Co, and on the experiment side, the proposed work can significantly enhance the scores of common classic 3DHR backbones up to -34 mm pose error suppression, putting them among the top list on the in-the-wild benchmark data. Such achievement shows that our approach helps unveil the true potential of the common classic 3DHR backbones. Based on these findings, we further investigate various settings on the proposed framework to better elaborate the capability of our collaborative strategy in the 3DHR task.

In this paper, we propose a novel disease detection framework based on translating a given chest X-ray (CXR) to a corresponding normal CXR image. To train a model for normal CXR translation, we synthesize a paired image dataset from existing public CXR datasets. More specifically, we synthesize new images that appear to be from the same patient but without abnormalities, i.e., matching-pairs, that are not present in the dataset. Here, we search for normal local regions in images contained within the dataset that have a similar appearance, and we apply a blending process to effectively erase the abnormal regions. A conditional GAN can then be trained on the synthetic matching-pair images for a normal CXR translator model that transforms the given CXR images into their corresponding normal CXRs. We incorporate this pretrained translator model into a deep learning based object detection model. Here we define the difference between the translated normal CXR and the given input as the residual-saliency map. As the residual-saliency map will be most active at diseased regions, we use this as the attention within the detection model. We also utilize the matching-pair synthesis pipeline to synthesize abnormal images for data augmentation when we train the detector. We evaluate the proposed approach on two public chest X-ray datasets: RSNA and VinBigData.

ProblemLow-quality fundus images with complex degredation can cause costly re-examinations of patients or inaccurate clinical diagnosis.AimThis study aims to create an automatic fundus macular image enhancement framework to improve low-quality fundus images and remove complex image degradation.MethodWe propose a new deep learning-based model that automatically enhances low-quality retinal fundus images that suffer from complex degradation. We collected a dataset, comprising 1068 pairs of high-quality (HQ) and low-quality (LQ) fundus images from the Kangbuk Samsung Hospital's health screening program and ophthalmology department from 2017 to 2019. Then, we used these dataset to develop data augmentation methods to simulate major aspects of retinal image degradation and to propose a customized convolutional neural network (CNN) architecture to enhance LQ images, depending on the nature of the degradation. Peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), r-value (linear index of fuzziness), and proportion of ungradable fundus photographs before and after the enhancement process are calculated to assess the performance of proposed model. A comparative evaluation is conducted on an external database and four different open-source databases.ResultsThe results of the evaluation on the external test dataset showed an significant increase in PSNR and SSIM compared with the original LQ images. Moreover, PSNR and SSIM increased by over 4 dB and 0.04, respectively compared with the previous state-of-the-art methods (P < 0.05). The proportion of ungradable fundus photographs decreased from 42.6% to 26.4% (P = 0.012).ConclusionOur enhancement process improves LQ fundus images that suffer from complex degradation significantly. Moreover our customized CNN achieved improved performance over the existing state-of-the-art methods. Overall, our framework can have a clinical impact on reducing re-examinations and improving the accuracy of diagnosis.

We formulate the visual tracking problem as a semi-supervised continual learning problem, where only an initial frame is labeled. In contrast to conventional meta-learning based approaches that regard visual tracking as an instance detection problem with a focus on finding good weights for model initialization, we consider both initialization and online update processes simultaneously under our adaptive continual meta-learning framework. The proposed adaptive meta-learning strategy dynamically generates the hyperparameters needed for fast initialization and online update to achieve more robustness via adaptively regulating the learning process. In addition, our continual meta-learning approach based on knowledge distillation scheme helps the tracker adapt to new examples while retaining its knowledge on previously seen examples. We apply our proposed framework to deep learning-based tracking algorithm to obtain noticeable performance gains and competitive results against recent state-of-the-art tracking algorithms while performing at real-time speeds.

Retinal artery–vein (AV) classification is a prerequisite for quantitative analysis of retinal vessels, which provides a biomarker for neurologic, cardiac, and systemic diseases, as well as ocular diseases. Although convolutional neural networks have presented remarkable performance on AV classification, it often comes with a topological error, like an abrupt class flipping on the same vessel segment or a weakness for thin vessels due to their indistinct appearances. In this paper, we present a new method for AV classification where the underlying vessel topology is estimated to give consistent prediction along the actual vessel structure. We cast the vessel topology estimation as iterative vascular connectivity prediction, which is implemented as deep-learning-based pairwise classification. In consequence, a whole vessel graph is separated into sub-trees, and each of them is classified as an artery or vein in whole via a voting scheme. The effectiveness and efficiency of the proposed method is validated by conducting experiments on two retinal image datasets acquired using different imaging techniques called DRIVE and IOSTAR.

In this paper, we present a novel cascaded classification framework for automatic detection of individual and clusters of microcalcifications (μC). Our framework comprises three classification stages: i) a random forest (RF) classifier for simple features capturing the second order local structure of individual μCs, where non-μC pixels in the target mammogram are efficiently eliminated; ii) a more complex discriminative restricted Boltzmann machine (DRBM) classifier for μC candidates determined in the RF stage, which automatically learns the detailed morphology of μC appearances for improved discriminative power; and iii) a detector to detect clusters of μCs from the individual μC detection results, using two different criteria. From the two-stage RF-DRBM classifier, we are able to distinguish μCs using explicitly computed features, as well as learn implicit features that are able to further discriminate between confusing cases. Experimental evaluation is conducted on the original Mammographic Image Analysis Society (MIAS) and mini-MIAS databases, as well as our own Seoul National University Bundang Hospital digital mammographic database. It is shown that the proposed method outperforms comparable methods in terms of receiver operating characteristic (ROC) and precision-recall curves for detection of individual μCs and free-response receiver operating characteristic (FROC) curve for detection of clustered μCs.

Estimation of the shape dissimilarity between 3D models is a very important problem in both computer vision and graphics for 3D surface reconstruction, modeling, matching, and compression. In this paper, we propose a novel method called surface roving technique to estimate the shape dissimilarity between 3D models. Unlike conventional methods, our surface roving approach exploits a virtual camera and Z-buffer, which is commonly used in 3D graphics. The corresponding points on different 3D models can be easily identified, and also the distance between them is determined efficiently, regardless of the representation types of the 3D models. Moreover, by employing the viewpoint sampling technique, the overall computation can be greatly reduced so that the dissimilarity is obtained rapidly without loss of accuracy. Experimental results show that the proposed algorithm achieves fast and accurate measurement of shape dissimilarity for different types of 3D object models.

We propose a new method to optimize the completely-trained boosted cascade detector on an enforced training set. Recently, due to the accuracy and real-time characteristics of boosted cascade detectors like the Adaboost, a lot of variant algorithms have been proposed to enhance the performance given a fixed number of training data. And, most of algorithms assume that a given training set well exhibits the real world distributions of the target and non-target instances. However, this is seldom true in real situations, and thus often causes higher false-classification ratio. In this paper, to solve the optimization problem of completely trained boosted cascade detector on false-classified instances, we propose a new base hypothesis weight optimization algorithm called DOOMRED (Direct Optimization Of Margin for Rare Event Detection) using a mathematically derived error upper bound of boosting algorithms. We apply the proposed algorithm to a cascade structured frontal face detector trained by AdaBoost algorithm. Experimental results demonstrate that the proposed algorithm has competitive ability to maintain accuracy and real-time characteristic of the boosted cascade detector compared to those of other heuristic approaches while requiring reasonably small amount of optimization time.

In most content-based image retrieval systems, the color information is extensively used for its simplicity and generality. Due to its compactness in characterizing the global information, a uniform quantization of colors, or a histogram, has been the most commonly used color descriptor. However, a cluster-based representation, or a signature, has been proven to be more compact and theoretically sound than a histogram for increasing the discriminatory power and reducing the gap between human perception and computer-aided retrieval system. Despite of these advantages, only few papers have broached dissimilarity measure based on the cluster-based nonuniform quantization of colors. In this paper, we extract the perceptual representation of an original color image, a statistical signature by modifying general color signature, which consists of a set of points with statistical volume. Also we present a novel dissimilarity measure for a statistical signature called Perceptually Modified Hausdorff Distance (PMHD) that is based on the Hausdorff distance. In the result, the proposed retrieval system views an image as a statistical signature, and uses the PMHD as the metric between statistical signatures. The precision versus recall results show that the proposed dissimilarity measure generally outperforms all other dissimilarity measures on an unmodified commercial image database.