Using image-level weakly supervised semantic segmentation (WSSS) techniques to segment tissue regions in giga-pixel histopathological whole slide images (WSI) has garnered widespread attention, as it can reduce many annotation workloads for pathologists. Most recent studies are based on class activation mapping (CAM) to generate pseudo masks, which are then used to train segmentation model in a fully supervised manner. However, it is still a challenge to accurately segment non-predominant tissue categories due to the existence of long-tailed and inter-class homogeneity matters. For these matters, we propose three designs to solve them: 1) Diffusion-based Data Generation to synthesis new images of tail class to expand data distribution; 2) Feature Recalibration to reassign the logits in CAM to narrow the feature-level prediction gap between predominant and non-predominant classes; 3) Grade-skip Learning to correct the under-fitting tendency of hard samples during the segmentation phase. Moreover, we also design a powerful pipeline LoHo for histopathology tissue segmentation. Extensive experiments demonstrate that our method not only achieves new state-of-the-art performances but also significantly improves segmentation of tail classes. In addition, our methods are plug-and-play, making it easily integrable into many mainstream WSSS frameworks.

Semi-supervised segmentation (S3) is one of the preferred choices for histopathological image segmentation tasks, while how to improve model’s learning capability for unlabeled data remains a key challenge in S3. The remarkable feature extraction abilities of Segment Anything Model (SAM) offers a potential opportunity. However, SAM’s performance on contextual complex histopathological images is not so desirable due to its limitations in finely capture structural relationships. To address this issue, we propose a novel SAM-based S3framework QuPaS, which consists of Quantum Force Field (QFF) Finetuning and Adversarial Estimation (AE). QFF covers the shortage of SAM’s limited understanding of spatial structure by simulating intermolecular forces to explore the structural topological relationships between pixel-level features. AE introduces an adversarial estimation network to align the consistency of confidence distributions between different outputs, thereby reducing the interference of incompatible semantic features on the model. Extensive experiments across three challenging histopathological segmentation scenarios have demonstrate that our QuPaS completely outperforms the state-of-the-art S3methods. Furthermore, QuPaS is able to maintain stable generalization performance on previously unseen domains. The code will be released at: https://github.com/director87/QuPaS.

Weakly supervised learning based on image-level labels can effectively reduce annotation costs, making it a popular choice for histopathological tissue segmentation. However, this pattern still face some challenges: 1) inaccurate class activation maps (CAM) make pseudo masks quality insufficient; 2) noisy pixels in pseudo masks will mislead the segmentation model's decision-making. To deal with these problems, we propose a novel weakly supervised semantic segmentation (WSSS) framework. First, we introduce Local Spatial Affine Perturbation to strengthen the model's utilization of weak supervision signals and improve its robustness to noisy regions within CAM. Second, we propose Wave-aware Dynamic Feature Aggregation to adaptively enhance the information-aware representation of target regions to obtain fine-grained pseudo masks enriched with positive semantic information. Third, we train a segmentation model with a noise-suppression scheme called Cross-scale Logits Distillation to reduce the inevitable false positive pixels in pseudo masks. We conduct extensive experiments to validate our method and set new state-of-the-art segmentation performances on five histopathological tissue segmentation datasets. Moreover, we will introduce a new dataset, GCSS-WSSS for gastric cancer, to promote the diversification for the research community of computational pathology. Code and data will be released at: https://github.com/director87/WaWeHis.

Cancer is a leading cause of death worldwide due to its aggressive nature and complex variability. Accurate prognosis is therefore challenging but essential for guiding personalized treatment and follow-up. Previous research often relied on single data sources, missing the opportunity to combine various types of patient information for more comprehensive survival predictions. To address these challenges, we propose a two-stage fusion method named Cross-Attention and Multimodal Low-Rank Interaction Fusion Framework (CA-MLIF). In the first stage, we propose a CA mechanism for real-time feature updates and cross-modal mutual learning to capture rich semantic information. In the second stage, we design a novel multimodal low-rank interaction fusion method for survival prediction. Specifically, we present modal attention mechanism (MAM) for feature filtration, low-rank multimodal fusion (LMF) for model complexity reduction, and optimal weight concatenation (OWC) for maximizing feature integration. Extensive experiments on two public datasets TCGA-GBMLGG and TCGA-KIRC, as well as a multi-center in-house lung adenocarcinoma (LUAD) dataset validate the effectiveness of CA-MLIF, which demonstrate that our method outperforms existing approaches in survival prediction under both pathology-gene fusion and CT-pathology fusion scenarios.

Image-level weakly supervised semantic segmentation (WSSS) reduces the dependence on high-quality data annotation, which plays a crucial role in computational pathology. Benefit from the ability to localize the objects with only binary labels, Class Activation Map (CAM) is a widely used method to initial pseudo masks. However, due to the low contrast among different tissues in histopathological images, most existing CAM-based methods perform poorly in gland segmentation. We retrospect this process and find that class consistency and semantic consistency can guide the network to effectively distinguish confusing pixels and generate fine-grained pseudo masks. Specifically, for class consistency, we propose Consistency Correlation Attention (CCA) to encourage the network to focus on the contribution of class features to semantic dependencies. For semantic consistency, we propose Multi-scale Pyramid Fusion Pooling (MPFP) to aggregate coarse-to-fine global semantic information from CAMs at multiple spatial resolutions, thus identifying class localization. Additionally, we introduce a Purified Labels Filtration (PLF) strategy during the segmentation phase to mitigate the noisy supervision signal and improve the segmentation quality of the model. Extensive experiments show that the our method achieves new state-of-the-art results on three publicly available gland datasets. Furthermore, our method demonstrates impressive domain adaptation capability, achieving satisfactory results with only a small portion of samples when faced with unseen domain data.

Mitosis counting is crucial for breast cancer grading and prognosis but is traditionally manual, time-consuming, and subjective. Existing methods focus on patch-level features and neglect important contextual and pathological information. In this paper, we propose a prior knowledge-assisted feature enhancement classification framework that first leverages nucleuslevel features to enhance image-level detection performance. Specifically, a nuclear graph is constructed with nuclei as nodes, where node features are designed based on prior knowledge of nuclear structural irregularities, density, cytoplasm properties, and staining characteristics. The Adjacent nuclei background is added as edge features to enhance contextual relationships. A graph neural network is applied to iteratively propagate node information and integrate edge features, with a Sequential Feature Learning (SFL) module further enhancing the dynamically updated nucleus representations into refined nucleus-level features. An inter-modal mutual learning (IML) is introduced to enable complementary learning between nucleus-level features and image-level features from a convolutional neural network. Experiments on MIDOG2021, MIDOG2022, and GZMH-V2 datasets show superior performance with weak supervision using only point labels.

To improve the overall survival rate of cancer patients, we propose an innovative approach named Multimodal Fusion Framework based on Low-rank Interaction (MF2LI), which aims to overcome the current limitations of relying solely on single-modal data prediction and the excessive complexity of fusion. By harnessing low-rank multimodal fusion (LMF) and optimal weight integration (OWI), MF2LI maximizes the integration of pathological images and genomic data. The model incorporates a parallel decomposition strategy, reducing complexity and facilitating fusion based on the contributions of each component. We validate our method using the GBMLGG and KIRC datasets from The Cancer Genome Atlas (TCGA). The C-index of the proposed model stands at $0.895 \pm 0.007$ and $0.728 \pm 0.030$ for the two datasets, respectively, outperforming existing methods. Furthermore, we generate visualizations of the risk ratios, which demonstrate a strong alignment with the actual grade classifications. Extensive experiments have shown that our model improves the prognosis prediction of tumor patients and has considerable clinical value.

In the field of gland segmentation in histopathology, deep-learning methods have made significant progress. However, most existing methods not only require a large amount of high-quality annotated data but also tend to confuse the internal of the gland with the background. To address this challenge, we propose a new semi-supervised method named DCCL-Seg for gland segmentation, which follows the teacher-student framework. Our approach can be divided into follows steps. First, we design a contrastive learning module to improve the ability of the student model's feature extractor to distinguish between gland and background features. Then, we introduce a Signed Distance Field (SDF) prediction task and employ dual-consistency strategy (across tasks and models) to better reinforce the learning of gland internal. Next, we proposed a pseudo label filtering and reweighting mechanism, which filters and reweights the pseudo labels generated by the teacher model based on confidence. However, even after reweighting, the pseudo labels may still be influenced by unreliable pixels. Finally, we further designed an assistant predictor to learn the reweighted pseudo labels, which do not interfere with the student model's predictor and ensure the reliability of the student model's predictions. Experimental results on the publicly available GlaS and CRAG datasets demonstrate that our method outperforms other semi-supervised medical image segmentation methods.

Nuclei segmentation is a fundamental and critical step in digital pathological image analysis. Fully supervised nuclei segmentation requires a lot of pixel-by-pixel manual annotation by pathologists, which is very time-consuming and laborious. To minimize the labeling burden of pathologists, this paper uses only point annotations of nuclei data for weakly supervised learning. Specifically, a two-stage model named EOFD-Net with feature denoising and edge optimization is proposed. In the first stage, three weak labels (K-means cluster labels, Voronoi labels, and superpixel labels) with complementary information are used to train the encoder-decoder network to achieve coarse segmentation of nuclei. A feature denoising module(FDM) is designed in the encoder part, which can effectively reduce noise interference. In the second stage, we designed an edge optimization strategy using the prior knowledge of the trained model in the first stage. Confident learning is employed to denoise pseudo-label and rectify the mislabel. These optimized labels are input into the second stage to obtain the final segmentation results. The performance of our method outperforms current state-of-the-art methods on two publicly nuclei segmentation datasets, MoNuSeg and TNBC.

Accurate and automated gland segmentation on pathological images can assist pathologists in diagnosing the malignancy of colorectal adenocarcinoma. However, due to various gland shapes, severe deformation of malignant glands, and overlapping adhesions between glands. Gland segmentation has always been very challenging. To address these problems, we propose a DEA model. This model consists of two branches: the backbone encoding and decoding network and the local semantic extraction network. The backbone encoding and decoding network extracts advanced Semantic features, uses the proposed feature decoder to restore feature space information, and then enhances the boundary features of the gland through boundary enhancement attention. The local semantic extraction network uses the pre-trained DeepLabv3+ as a Local semantic-guided encoder to realize the extraction of edge features. Experimental results on two public datasets, GlaS and CRAG, confirm that the performance of our method is better than other gland segmentation methods.

The topology integrity of blood vessel segmentation is crucial for clinical disease diagnosis. However, existing methods for enhancing vessel topology have overlooked the degree and type of topology interaction, and have not incorporated topology contrast relation into consideration. In this letter, we propose confindence-based topolopy interaction and topology contrast loss method to enhance the interaction and contrast relations for intra-class and inter-class of topology structure. Additionally, considering the influence of multiscale feature on topology learning, we propose lightweight global feature extraction and align fusion to better capture global features and mitigate feature misalignment. The quantitative and qualitative comparisons on the DRIVE and STARE datasets show the superiority of the proposed model. Furthermore, the improvements experiments of the advanced topology enhancement networks using our proposed methods, and ablation experiments on DCA1 datasets provide evidence of the effectiveness of the proposed method.

Deep learning-based vessel segmentation tasks serve as important auxiliary tools for disease diagnosis. However, the region connecting the foreground and background, which is named the edge-buffer region in this letter, suffers from noisy labels and a lack of discriminative features due to low contrast and limitations of imaging devices. To address these limitations, we propose a self-correcting region supervision to revamp the noisy labels in the edge-buffer region. Furthermore, we introduce the concept of treating the edge-buffer region independently from the foreground and background, leveraging the designed contrastive learning method and edge-blur-guided module to enhance discriminative learning ability and collaborative learning ability across different regions, respectively. The experimental results comparison with other classical and state-of-the-art methods on DRIVE, CHASEDB1, and DCA1 datasets has proven the effectiveness of the proposed methods.

Underwater images typically suffer from various quality degradation issues due to the scattering and absorption of light, but these degraded-quality underwater images are unbeneficial for analysis and applications. To effectively solve these quality degradation issues, an underwater image enhancement method via weighted wavelet visual perception fusion is introduced, called WWPF. Concretely, we first present an attenuation-map-guided color correction strategy to correct the color distortion of an underwater image. Subsequently, we employ the maximum information entropy optimized global contrast strategy to the color-corrected image to obtain a global contrast-enhanced image. Meanwhile, we apply a fast integration optimized local contrast strategy to the color-corrected image to get a local contrast-enhanced image. To exploit the complementary of the global contrast-enhanced image and the local contrast-enhanced image, we introduce a weighted wavelet visual perception fusion strategy to obtain a high-quality underwater image by fusing the high-frequency and low-frequency components of images at different scales. Our extensive experiments on three benchmarks validate that our WWPF outperforms the state-of-the-art methods in qualitative and quantitative. Besides, the underwater images processed by our WWPF also benefit practical underwater applications. The code is availablehttps://github.com/Li-Chongyi/WWPF_code.

Self-supervised learning (SSL) has demonstrated its power in generalized model acquisition by leveraging the discriminative semantic and explicit positional information of unlabeled datasets. Unfortunately, mainstream contrastive learning-based methods excessive focus on semantic information and ignore the position is also the carrier of image content, resulting in inadequate data utilization and extensive computational consumption. To address these issues, we present an efficient SSL framework, learning What and Where to learn (W2SSL), to aggregate semantic and position features. Concretely, we devise a spatially-coupled sampling manner to process images through pre-defined rules, which integrates the advantage of semantic (What) and positional (Where) features into framework to enrich the diversity of feature representation capabilities and improve data utilization. Besides, a spectrum of latent vectors is obtained by mapping the positional features, which implicitly explores the relationship between these vectors. Whereafter, the corresponding discriminative and contrastive optimization objectives are seamlessly embedded in the framework via a cascade paradigm to explore semantic and positional features. The proposed W2SSL is verified on different types of datasets, which demonstrates that it still outperforms state-of-the-art SSL methods even with half the computational consumption. Code will be available at https://github.com/WilyZhao8/W2SSL.

Stroke is a leading cause of disability and fatality in the world, with ischemic stroke being the most common type. Digital Subtraction Angiography images, the gold standard in the operation process, can accurately show the contours and blood flow of cerebral vessels. The segmentation of cerebral vessels in DSA images can effectively help physicians assess the lesions. However, due to the disturbances in imaging parameters and changes in imaging scale, accurate cerebral vessel segmentation in DSA images is still a challenging task. In this paper, we propose a novel Edge Regularization Network (ERNet) to segment cerebral vessels in DSA images. Specifically, ERNet employs the erosion and dilation processes on the original binary vessel annotation to generate pseudo-ground truths of False Negative and False Positive, which serve as constraints to refine the coarse predictions based on their mapping relationship with the original vessels. In addition, we exploit a Hybrid Fusion Module based on convolution and transformers to extract local features and build long-range dependencies. Moreover, to support and advance the open research in the field of ischemic stroke, we introduce FPDSA, the first pixel-level semantic segmentation dataset for cerebral vessels. Extensive experiments on FPDSA illustrate the leading performance of our ERNet.

Ischemic stroke has a very high mortality and disability rate, and intracranial artery stenosis is an important cause of ischemic stroke. At present, transvascular interventional surgery is an effective remedy to treat intracranial artery stenosis, and as the gold standard in surgery, Digital Subtraction Angiography (DSA) images can effectively display the outline of blood vessels and the flow of blood. Detecting and locating the stenosis from DSA images is a challenging problem due to the large variation in the thickness of the blood vessel and the complex shape of the blood vessel. In this paper, we collect a dataset with 2860 DSA sequence samples and annotate stenosis locations, constructing the first automatic detection and localization method for stenosis in DSA images. In addition, considering that the commonly used Intersection-over-Union (IoU) loss ignores the similarity indicators of the image patches in the prediction box and the ground-truth (GT) box, a plug-and-play loss function that considers the image similarity between the prediction box and the GT box is proposed to effectively improve network performance. Extensive experiments demonstrate the effectiveness of our approach, which outperforms classical detectors.

Abstract Lowlight images with low brightness and contrast, blurry details usually bring us an uncomfortable visual experience. To promote the quality of these deviation images, this paper presents a new and efficient approach, named MFMR, for enhancing lowlight images in the hue‐saturation‐value (HSV) colour space. Concretely, the multi‐angle filter is first applied to estimate the artifact‐free illumination and reflection component of the V‐channel. Afterward, the adaptive bi‐interval histogram with human visual characteristics and morphological operations is employed to process the former, adaptive gamma correction to process the latter for generating various feature maps. In the end, these feature maps are united via adaptive multi‐scale fusion strategy to reconstruct high‐quality images, which are characterized by high contrast and brightness, vivid colour, and clearer details. Extensive experiments show that this method is a well‐proven low‐light image enhancement approach, which outperforms the state‐of‐the‐art comparison methods. Furthermore, the proposed method also can yield satisfying images in the heavy foggy, yellow sand, underwater, and other severe conditions.

Brain tumor segmentation (BTS) in magnetic resonance image (MRI) is crucial for brain tumor diagnosis, cancer management and research purposes. With the great success of the ten-year BraTS challenges as well as the advances of CNN and Transformer algorithms, a lot of outstanding BTS models have been proposed to tackle the difficulties of BTS in different technical aspects. However, existing studies hardly consider how to fuse the multi-modality images in a reasonable manner. In this paper, we leverage the clinical knowledge of how radiologists diagnose brain tumors from multiple MRI modalities and propose a clinical knowledge-driven brain tumor segmentation model, called CKD-TransBTS. Instead of directly concatenating all the modalities, we re-organize the input modalities by separating them into two groups according to the imaging principle of MRI. A dual-branch hybrid encoder with the proposed modality-correlated cross-attention block (MCCA) is designed to extract the multi-modality image features. The proposed model inherits the strengths from both Transformer and CNN with the local feature representation ability for precise lesion boundaries and long-range feature extraction for 3D volumetric images. To bridge the gap between Transformer and CNN features, we propose a Trans&CNN Feature Calibration block (TCFC) in the decoder. We compare the proposed model with six CNN-based models and six transformer-based models on the BraTS 2021 challenge dataset. Extensive experiments demonstrate that the proposed model achieves state-of-the-art brain tumor segmentation performance compared with all the competitors.

Ultrasonography is an important routine examination for breast cancer diagnosis, due to its non-invasive, radiation-free and low-cost properties. However, the diagnostic accuracy of breast cancer is still limited due to its inherent limitations. Then, a precise diagnose using breast ultrasound (BUS) image would be significant useful. Many learning-based computer-aided diagnostic methods have been proposed to achieve breast cancer diagnosis/lesion classification. However, most of them require a pre-define region of interest (ROI) and then classify the lesion inside the ROI. Conventional classification backbones, such as VGG16 and ResNet50, can achieve promising classification results with no ROI requirement. But these models lack interpretability, thus restricting their use in clinical practice. In this study, we propose a novel ROI-free model for breast cancer diagnosis in ultrasound images with interpretable feature representations. We leverage the anatomical prior knowledge that malignant and benign tumors have different spatial relationships between different tissue layers, and propose a HoVer-Transformer to formulate this prior knowledge. The proposed HoVer-Trans block extracts the inter- and intra-layer spatial information horizontally and vertically. We conduct and release an open dataset GDPH&SYSUCC for breast cancer diagnosis in BUS. The proposed model is evaluated in three datasets by comparing with four CNN-based models and three vision transformer models via five-fold cross validation. It achieves state-of-the-art classification performance (GDPH&SYSUCC AUC: 0.924, ACC: 0.893, Spec: 0.836, Sens: 0.926) with the best model interpretability. In the meanwhile, our proposed model outperforms two senior sonographers on the breast cancer diagnosis when only one BUS image is given (GDPH&SYSUCC-AUC ours: 0.924 vs. reader1: 0.825 vs. reader2: 0.820).

Vessel segmentation is essential for clinical diagnosis and surgical planning. However, it is quite challenging for automatic blood vessel segmentation due to low contrast, complex structure, and variable scale, especially when the annotated data is scarce. In this paper, we propose a novel multiscale attention aggregation network (MAA-Net) for vessel segmentation. In MAA-Net, based on a U-shaped encoder-decoder architecture, the dual attention module with scale factors is employed behind the decoder at each stage to generate multi-resolution feature maps adaptively weighted by channel and location attention. In this way, multiscale contextual information with long-range dependencies can be captured to tackle scale variations of vessels. Meanwhile, these attention feature maps are gradually integrated into multi-level aggregate supervision to assemble multiscale context information for refining segmentation results. The proposed method was evaluated on the retinal vessel and coronary angiography dataset (DRIVE and DCA1). Results demonstrate that MAA-Net achieves state-of-the-art performance for vessel segmentation. The code will be available at: https://github.com/lseventeen/MAA-Net-Vessel-Segmentation.

Tissue-level semantic segmentation is a vital step in computational pathology. Fully-supervised models have already achieved outstanding performance with dense pixel-level annotations. However, drawing such labels on the giga-pixel whole slide images is extremely expensive and time-consuming. In this paper, we use only patch-level classification labels to achieve tissue semantic segmentation on histopathology images, finally reducing the annotation efforts. We propose a two-step model including a classification and a segmentation phases. In the classification phase, we propose a CAM-based model to generate pseudo masks by patch-level labels. In the segmentation phase, we achieve tissue semantic segmentation by our propose Multi-Layer Pseudo-Supervision. Several technical novelties have been proposed to reduce the information gap between pixel-level and patch-level annotations. As a part of this paper, we introduce a new weakly-supervised semantic segmentation (WSSS) dataset for lung adenocarcinoma (LUAD-HistoSeg). We conduct several experiments to evaluate our proposed model on two datasets. Our proposed model outperforms five state-of-the-art WSSS approaches. Note that we can achieve comparable quantitative and qualitative results with the fully-supervised model, with only around a 2% gap for MIoU and FwIoU. By comparing with manual labeling on a randomly sampled 100 patches dataset, patch-level labeling can greatly reduce the annotation time from hours to minutes. The source code and the released datasets are available at: https://github.com/ChuHan89/WSSS-Tissue.

Vessel segmentation is critical for disease diagnosis and surgical planning. Recently, the vessel segmentation method based on deep learning has achieved outstanding performance. However, vessel segmentation remains challenging due to thin vessels with low contrast that easily lose spatial information in the traditional U-shaped segmentation network. To alleviate this problem, we propose a novel and straightforward full-resolution network (FR-UNet) that expands horizontally and vertically through a multiresolution convolution interactive mechanism while retaining full image resolution. In FR-UNet, the feature aggregation module integrates multiscale feature maps from adjacent stages to supplement high-level contextual information. The modified residual blocks continuously learn multiresolution representations to obtain a pixel-level accuracy prediction map. Moreover, we propose the dual-threshold iterative algorithm (DTI) to extract weak vessel pixels for improving vessel connectivity. The proposed method was evaluated on retinal vessel datasets (DRIVE, CHASE_DB1, and STARE) and coronary angiography datasets (DCA1 and CHUAC). The results demonstrate that FR-UNet outperforms state-of-the-art methods by achieving the highest Sen, AUC, F1, and IOU on most of the above-mentioned datasets with fewer parameters, and that DTI enhances vessel connectivity while greatly improving sensitivity. The code is available at: https://github.com/lseventeen/FR-UNet.

Histopathological tissue classification is a simpler way to achieve semantic segmentation for the whole slide images, which can alleviate the requirement of pixel-level dense annotations. Existing works mostly leverage the popular CNN classification backbones in computer vision to achieve histopathological tissue classification. In this paper, we propose a super lightweight plug-and-play module, named Pyramidal Deep-Broad Learning (PDBL), for any well-trained classification backbone to improve the classification performance without a re-training burden. For each patch, we construct a multi-resolution image pyramid to obtain the pyramidal contextual information. For each level in the pyramid, we extract the multi-scale deep-broad features by our proposed Deep-Broad block (DB-block). We equip PDBL in three popular classification backbones, ShuffLeNetV2, EfficientNetb0, and ResNet50 to evaluate the effectiveness and efficiency of our proposed module on two datasets (Kather Multiclass Dataset and the LC25000 Dataset). Experimental results demonstrate the proposed PDBL can steadily improve the tissue-level classification performance for any CNN backbones, especially for the lightweight models when given a small among of training samples (less than 10%). It greatly saves the computational resources and annotation efforts. The source code is available at: https://github.com/linjiatai/PDBL.

Retinal vessel segmentation is of great significance for the clinical diagnosis of eye-related diseases and diabetic retinopathy. CNN-based models have led to advancements in the task of retinal vessel segmentation in recent years, but such methods typically miss high-frequency information such as object edges and delicate features, which are critical for vessel segmentation. Therefore, we present a Dual-stream Edge Complementary Network (DECNet) with a novel Edge Complementary Module (ECM) to better tackle these challenges. Specifically, in DECNet, an edge regression stream is added to regress the edges of vessels, and the regression result can be feedback to the original body stream to replenish the missing and coarse edges in the segmentation results. Moreover, the ECM is designed to enhance the interaction between the body stream and the edge stream, and exploit more complementary information. Extensive experiment results on DRIVE and CHASEDB1 not only demonstrate the effectiveness of the proposed DECNet but also indicate that our DECNet outperforms other state-of-the-art approaches.

Pedestrian detection based on visual sensors has made significant progress, in which region proposal is the key step. There are two mainstream methods to generate region proposals: anchor‐based and anchor‐free. However, anchor‐based methods need more hyperparameters related to anchors for training compared with anchor‐free methods. In this paper, we propose a novel multiscale anchor‐free (MSAF) region proposal network to obtain proposals, especially for small‐scale pedestrians. It usually has several branches to predict proposals and assigns ground truth according to the height of pedestrian. Each branch consists of two components: one is feature extraction, and the other is detection head. Adapted channel feature fusion (ACFF) is proposed to select features at different levels of the backbone to effectively extract features. The detection head is used to predict the pedestrian center location, center offsets, and height to get bounding boxes. With our classifier, the detection performance can be further improved, especially for small‐scale pedestrians. The experiments on the Caltech and CityPersons demonstrate that the MSAF can significantly boost the pedestrian detection performance and the log‐average miss rate (MR) on the reasonable setting is 3.97% and 9.5%, respectively. If proposals are reclassified with our classifier, MR is 3.38% and 8.4%. The detection performance can be further improved, especially for small‐scale pedestrians.

Ray-casting algorithm is an important volume rendering algorithm, which is widely used in medical image processing. Aiming to address the shortcomings of the current ray-casting algorithms in 3D reconstruction of medical images, such as slow rendering speed and low sampling efficiency, an improved algorithm based on dynamic adaptive sampling is proposed. By using the central difference gradient method, the corresponding sampling interval is obtained dynamically according to the different sampling points. Meanwhile, a new rendering operator is proposed based on the color value and opacity changes before and after the ray enters the volume element, and the resistance luminosity. Compared with the state of other algorithms, experimental results show that the method proposed in this paper has a faster rendering speed while ensuring the quality of the generated image.