Automatic colorization techniques often struggle with dual-character line art, particularly in areas such as color coordination between characters, handling complex scenes and interactions, and meeting personalized colorization requirements. To address these challenges, we introduce DuCol, a novel framework specifically designed for the colorization of dual-character line art. DuCol leverages text-based inputs to accommodate personalized color preferences and integrates a Text-Labeled Adaptive Colorization (TAC) Module to ensure global color assignment, effectively harmonizing colors between characters. Furthermore, the model utilizes detailed segmentation information from a skeleton graph to enable precise boundary detection, resolving interactions between characters and preventing color bleeding or ambiguity. Extensive experiments on a large-scale illustration dataset demonstrate that DuCol’s superiority in dual-character line art colorization, establishing it as a leading solution in this domain.

Image semantic segmentation is ubiquitously used in scene understanding applications, such as AI Camera, which require high accuracy and efficiency. Deep learning has significantly advanced the state-of-the-art in semantic segmentation. However, many of recent semantic segmentation works only consider class accuracy and ignore the accuracies at the boundaries between semantic classes. To improve the semantic boundary accuracy, we propose low complexity Deep Guided Decoder (DGD) networks, trained with a novel Semantic Boundary-Aware Learning (SBAL) strategy. Our ablation studies on Cityscapes and the ADE20K-32 confirm the effectiveness of our approach with network of different complexities. We show that our DeepGBASS approach significantly improves the mIoU by up to 11% relative gain and the mean boundary F1-score (mBF) by up to 39.4% when training MobileNetEdgeTPU DeepLab on ADE20K-32 dataset.

Fabric defect detection is an intriguing but challenging topic. Many methods have been proposed for fabric defect detection, but these methods are still suboptimal due to the complex diversity of both fabric textures and defects. In this paper, we propose a generative adversarial network (GAN)-based framework for fabric defect detection. Considering existing challenges in real-world applications, the proposed fabric defect detection system is capable of learning existing fabric defect samples and automatically adapting to different fabric textures during different application periods. Specifically, we customize a deep semantic segmentation network for fabric defect detection that can detect different defect types. Furthermore, we attempted to train a multistage GAN to synthesize reasonable defects in new defect-free samples. First, a texture-conditioned GAN is trained to explore the conditional distribution of defects given different texture backgrounds. Given a novel fabric, we aim to generate reasonable defective patches. Then, a GAN-based fusion network fuses the generated defects to specific locations. Finally, the well-trained multistage GAN continuously updates the existing fabric defect datasets and contributes to the fine-tuning of the semantic segmentation network to better detect defects under different conditions. Comprehensive experiments on various representative fabric samples are conducted to verify the detection performance of our proposed method.

Idiopathic inflammatory myopathy (IIM) is a common skeletal muscle disease that relates to weakness and inflammation of muscle. Early diagnosis and prognosis of different types of IIMs will guide the effective treatment. Interpretation of digitized images of the cross-section muscle biopsy, which is currently done manually, provides the most reliable diagnostic information. With the increasing volume of images, the management and manual interpretation of the digitized muscle images suffer from low efficiency and high interobserver variabilities. In order to address these problems, we propose the first complete framework of automatic IIM diagnosis system for the management and interpretation of digitized skeletal muscle histopathology images. The proposed framework consists of several key components: (1) Automatic cell segmentation, perimysium annotation, and nuclei detection; (2) histogram-based feature extraction and quantification; (3) content-based image retrieval to search and retrieve similar cases in the database for comparative study; and (4) majority voting-based classification to provide decision support for computer-aided clinical diagnosis. Experiments show that the proposed diagnosis system provides efficient and robust interpretation of the digitized muscle image and computer-aided diagnosis of IIM.

Computerized microscopy image analysis plays an important role in computer aided diagnosis and prognosis. Machine learning techniques have powered many aspects of medical investigation and clinical practice. Recently, deep learning is emerging as a leading machine learning tool in computer vision and has attracted considerable attention in biomedical image analysis. In this paper, we provide a snapshot of this fast-growing field, specifically for microscopy image analysis. We briefly introduce the popular deep neural networks and summarize current deep learning achievements in various tasks, such as detection, segmentation, and classification in microscopy image analysis. In particular, we explain the architectures and the principles of convolutional neural networks, fully convolutional networks, recurrent neural networks, stacked autoencoders, and deep belief networks, and interpret their formulations or modelings for specific tasks on various microscopy images. In addition, we discuss the open challenges and the potential trends of future research in microscopy image analysis using deep learning.

Successful diagnostic and prognostic stratification, treatment outcome prediction, and therapy planning depend on reproducible and accurate pathology analysis. Computer aided diagnosis (CAD) is a useful tool to help doctors make better decisions in cancer diagnosis and treatment. Accurate cell detection is often an essential prerequisite for subsequent cellular analysis. The major challenge of robust brain tumor nuclei/cell detection is to handle significant variations in cell appearance and to split touching cells. In this paper, we present an automatic cell detection framework using sparse reconstruction and adaptive dictionary learning. The main contributions of our method are: 1) A sparse reconstruction based approach to split touching cells; 2) An adaptive dictionary learning method used to handle cell appearance variations. The proposed method has been extensively tested on a data set with more than 2000 cells extracted from 32 whole slide scanned images. The automatic cell detection results are compared with the manually annotated ground truth and other state-of-the-art cell detection algorithms. The proposed method achieves the best cell detection accuracy with a F1 score = 0.96.

Computer-aided diagnosis of medical images requires thorough analysis of image details. For example, examining all cells enables fine-grained categorization of histopathological images. Traditional computational methods may have efficiency issues when performing such detailed analysis. In this paper, we propose a robust and scalable solution to achieve this. Specifically, a robust segmentation method is developed to delineate region-of-interests (e.g., cells) accurately, using hierarchical voting and repulsive active contour. A hashing-based large-scale retrieval approach is also designed to examine and classify them by comparing with a massive training database. We evaluate this proposed framework on a challenging and important clinical use case, i.e., differentiation of two types of lung cancers (the adenocarcinoma and the squamous carcinoma), using thousands of histopathological images extracted from hundreds of patients. Our method has achieved promising performance, i.e., 87.3% accuracy and 1.68 seconds by searching among half-million cells.

BACKGROUND: Non-small cell lung cancer (NSCLC), the most common type of lung cancer, is one of serious diseases causing death for both men and women. Computer-aided diagnosis and survival prediction of NSCLC, is of great importance in providing assistance to diagnosis and personalize therapy planning for lung cancer patients. RESULTS: In this paper we have proposed an integrated framework for NSCLC computer-aided diagnosis and survival analysis using novel image markers. The entire biomedical imaging informatics framework consists of cell detection, segmentation, classification, discovery of image markers, and survival analysis. A robust seed detection-guided cell segmentation algorithm is proposed to accurately segment each individual cell in digital images. Based on cell segmentation results, a set of extensive cellular morphological features are extracted using efficient feature descriptors. Next, eight different classification techniques that can handle high-dimensional data have been evaluated and then compared for computer-aided diagnosis. The results show that the random forest and adaboost offer the best classification performance for NSCLC. Finally, a Cox proportional hazards model is fitted by component-wise likelihood based boosting. Significant image markers have been discovered using the bootstrap analysis and the survival prediction performance of the model is also evaluated. CONCLUSIONS: The proposed model have been applied to a lung cancer dataset that contains 122 cases with complete clinical information. The classification performance exhibits high correlations between the discovered image markers and the subtypes of NSCLC. The survival analysis demonstrates strong prediction power of the statistical model built from the discovered image markers.

Accurate and robust detection of myonuclei in isolated single muscle fibers is required to calculate myonuclear domain size. However, this task is challenging because: 1) shape and size variations of the nuclei, 2) overlapping nuclear clumps, and 3) multiple z-stack images with out-of-focus regions. In this paper, we have proposed a novel automatic detection algorithm to robustly quantify myonuclei in isolated single skeletal muscle fibers. The original z-stack images are first converted into one all-in-focus image using multi-focus image fusion. A sufficient number of ellipse fitting hypotheses are then generated from the myonuclei contour segments using heteroscedastic errors-in-variables (HEIV) regression. A set of representative training samples and a set of discriminative features are selected by a two-stage sparse model. The selected samples with representative features are utilized to train a classifier to select the best candidates. A modified inner geodesic distance based mean-shift clustering algorithm is used to produce the final nuclei detection results. The proposed method was extensively tested using 42 sets of z-stack images containing over 1,500 myonuclei. The method demonstrates excellent results that are better than current state-of-the-art approaches.

Many spectrum sensing and access schemes have been proposed in recent years for opportunistic spectrum access (OSA). Most of them model the spectrum sensing and access problem as a partially observed Markov decision process (POMDP), where the channel statistics are assumed to be known to the secondary users (SU). However, few of them have considered the attackers who can launch effective jamming attacks by exploiting the same information that SUs have. In this paper, we study the problem of anti-jamming dynamic multichannel access in cognitive radio networks (CRNs) as a multiple-armed bandit (MAB) problem, where the transmitter and receiver adaptively choose arms (i.e., sending and receiving channels) to operate based on the common acknowledgment information. The proposed scheme enables the transmitter and receiver to hop to the same set of channels with high probability. Our simulation results show that the proposed scheme is resilient to various jamming attacks, and the SU transmitter and receiver can develop common knowledge on channel availability through online learning.

Recently, there has been great interest in physical layer security techniques that exploit the randomness of wireless channels for securely extracting cryptographic keys. Several interesting approaches have been developed and demonstrated for their feasibility. The state-of-the-art, however, still has much room for improving their practicality. This is because i) the key bit generation rate supported by most existing approaches is very low which significantly limits their practical usage given the intermittent connectivity in mobile environments; ii) existing approaches suffer from the scalability and flexibility issues, i.e., they cannot be directly extended to support efficient group key generation and do not suit for static environments. With these observations in mind, we present a new secret key generation approach that utilizes the uniformly distributed phase information of channel responses to extract shared cryptographic keys under narrowband multipath fading models. The proposed approach enjoys a high key bit generation rate due to its efficient introduction of multiple randomized phase information within a single coherence time interval as the keying sources. The proposed approach also provides scalability and flexibility because it relies only on the transmission of periodical extensions of unmodulated sinusoidal beacons, which allows effective accumulation of channel phases across multiple nodes. The proposed scheme is thoroughly evaluated through both analytical and simulation studies. Compared to existing work that focus on pairwise key generation, our approach is highly scalable and can improve the analytical key bit generation rate by a couple of orders of magnitude.