Chu Han

Chu Han
Guangdong provincial people's hospital · Radiology

Doctor of Philosophy

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53
Publications
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378
Citations

Publications

Publications (53)
Article
Color inconsistency is an inevitable challenge in computational pathology, which harms the pathological image analysis methods, especially the learning-based models. A series of approaches have been proposed for stain normalization. However, most of them are lack of flexibility in practice. In this paper, we formulated stain normalization as a digi...
Article
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 la...
Preprint
Full-text available
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 dif...
Article
Full-text available
Automatic tissue segmentation in whole-slide images (WSIs) is a critical task in hematoxylin and eosin- (H&E-) stained histopathological images for accurate diagnosis and risk stratification of lung cancer. Patch classification and stitching the classification results can fast conduct tissue segmentation of WSIs. However, due to the tumour heteroge...
Article
Objectives: To investigate whether breast edema characteristics at preoperative T2-weighted imaging (T2WI) could help evaluate axillary lymph node (ALN) burden in patients with early-stage breast cancer. Methods: This retrospective study included women with clinical T1 and T2 stage breast cancer and preoperative MRI examination in two independen...
Article
Full-text available
Background High immune infiltration is associated with favourable prognosis in patients with non-small-cell lung cancer (NSCLC), but an automated workflow for characterizing immune infiltration, with high validity and reliability, remains to be developed. Methods We performed a multicentre retrospective study of patients with completely resected N...
Preprint
Full-text available
Ultrasonography is an important routine examination for breast cancer diagnosis, due to its non-invasive, radiation-free and low-cost properties. However, it is still not the first-line screening test for breast cancer due to its inherent limitations. It would be a tremendous success if we can precisely diagnose breast cancer by breast ultrasound i...
Article
Cells/nuclei deliver massive information of microenvironment. An automatic nuclei segmentation approach can reduce pathologists’ workload and allow precise quantization of the microenvironment for biological and clinical researches. Existing deep learning models have achieved outstanding performance under the supervision of large amount of labeled...
Preprint
Full-text available
Lung cancer is the leading cause of cancer death worldwide, and adenocarcinoma (LUAD) is the most common subtype. Exploiting the potential value of the histopathology images can promote precision medicine in oncology. Tissue segmentation is the basic upstream task of histopathology image analysis. Existing deep learning models have achieved superio...
Article
Purpose Intra-tumoral tertiary lymphoid structures (TLSs) are associated with a favorable prognosis for patients with hepatocellular carcinoma (HCC). We aimed to identify image features related to TLSs and develop a nomogram for preoperative noninvasive prediction of intra-tumoral TLSs. Methods This retrospective study enrolled patients with HCC w...
Article
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 p...
Preprint
Color inconsistency is an inevitable challenge in computational pathology, which generally happens because of stain intensity variations or sections scanned by different scanners. It harms the pathological image analysis methods, especially the learning-based models. A series of approaches have been proposed for stain normalization. However, most o...
Preprint
Full-text available
Nuclear segmentation and classification is an essential step for computational pathology. TIA lab from Warwick University organized a nuclear segmentation and classification challenge (CoNiC) for H&E stained histopathology images in colorectal cancer based on the Lizard dataset. In this challenge, computer algorithms should be able to segment and r...
Article
Objective: This study aimed to establish a method to predict the overall survival (OS) of patients with stage I-III colorectal cancer (CRC) through coupling radiomics analysis of CT images with the measurement of tumor ecosystem diversification. Methods: We retrospectively identified 161 consecutive patients with stage I-III CRC who had underwen...
Article
Full-text available
Reliable nasopharyngeal carcinoma (NPC) segmentation plays an important role in radiotherapy planning. However, recent deep learning methods fail to achieve satisfactory NPC segmentation in magnetic resonance (MR) images, since NPC is infiltrative and typically has a small or even tiny volume with indistinguishable border, making it indiscernible f...
Preprint
Full-text available
Histopathological tissue classification is a fundamental task in pathomics cancer research. Precisely differentiating different tissue types is a benefit for the downstream researches, like cancer diagnosis, prognosis and etc. Existing works mostly leverage the popular classification backbones in computer vision to achieve histopathological tissue...
Article
Full-text available
Background Profound heterogeneity in prognosis has been observed in colorectal cancer (CRC) patients with intermediate levels of disease (stage II–III), advocating the identification of valuable biomarkers that could improve the prognostic stratification. This study aims to develop a deep learning-based pipeline for fully automatic quantification o...
Preprint
Full-text available
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 la...
Chapter
Semi-supervised learning has been recently employed to solve problems from medical image segmentation due to challenges in acquiring sufficient manual annotations, which is an important prerequisite for building high-performance deep learning methods. Since unlabeled data is generally abundant, most existing semi-supervised approaches focus on how...
Article
Full-text available
Background Distinguishing anorectal malignant melanoma from low rectal cancer remains challenging due to the overlap of clinical symptoms and imaging findings. We aim to investigate whether combining quantitative and qualitative magnetic resonance imaging (MRI) features could differentiate anorectal malignant melanoma from low rectal cancer. Method...
Article
To address the color cast and low contrast of underwater images, we propose a color correction and adaptive contrast enhancement algorithm for underwater image enhancement. In our work, we first design the dedicated fractions to compensate the lower color channels, which are calculated by considering the ratio of the differences between the upper a...
Article
Diabetic retinopathy, age-related macular degeneration and glaucoma, are the leading cauor head movements. The problem turns out to be more severe if the images were corrupted by ill conditions on eyes, such as micro-bleeding and plaques. To tackle this problem, we propose a multi-source registration model for retinal fundus images. Our proposed me...
Chapter
The accurate classification of gliomas is essential in clinical practice. It is valuable for clinical practitioners and patients to choose the appropriate management accordingly, promoting the development of personalized medicine. In the MICCAI 2020 Combined Radiology and Pathology Classification Challenge, 4 MRI sequences and a WSI image are provi...
Article
Scene labeling or parsing aims to assign pixelwise semantic labels for an input image. Existing CNN-based models cannot leverage the label dependencies, while RNN-based models predict labels within the local context. In this paper, we propose a fast LSTM scene labeling network via structural inference. A minimum spanning tree is used to build the i...
Article
Objectives: To develop and validate a radiomics nomogram for preoperative prediction of tumor histologic grade in gastric adenocarcinoma (GA). Methods: This retrospective study enrolled 592 patients with clinicopathologically confirmed GA (low-grade: n=154; high-grade: n=438) from January 2008 to March 2018 who were divided into training (n=450)...
Article
Multi-view clustering seeks an underlying partition of the data from multiple views. Organizing the data into a tensor and then learning a self-expressive latent one to exploit cross-view information has attracted much attention. Most of the recent works mainly focus on the tensor representation, but they fail to directly extract the task-driving a...
Chapter
In this paper, we propose a simple yet effective approach, named Triple Excitation Network, to reinforce the training of video salient object detection (VSOD) from three aspects, spatial, temporal, and online excitations. These excitation mechanisms are designed following the spirit of curriculum learning and aim to reduce learning ambiguities at t...
Article
With the explosive growth of action categories, zero-shot action recognition aims to extend a well-trained model to novel/unseen classes. To bridge the large knowledge gap between seen and unseen classes, in this brief, we visually associate unseen actions with seen categories in a visually connected graph, and the knowledge is then transferred fro...
Article
Integrative multi-view subspace clustering aims to partition observed samples into underlying clusters through fusing representative subspace information from different views into a latent space. The clustering performance relies on the accuracy of sample affinity measurement. However, existing approaches leverage the subspace representation of eac...
Preprint
In this paper, we propose a simple yet effective approach, named Triple Excitation Network, to reinforce the training of video salient object detection (VSOD) from three aspects, spatial, temporal, and online excitations. These excitation mechanisms are designed following the spirit of curriculum learning and aim to reduce learning ambiguities at t...
Article
In this paper, we aim to generate a video preview from a single image by proposing two cascaded networks, the Motion Embedding Network and the Motion Expansion Network. The Motion Embedding Network aims to embed the spatio-temporal information into an embedded image, called video snapshot. On the other end, the Motion Expansion Network is proposed...
Article
Full-text available
Ambient occlusion (abbr. AO) plays an important role in realistic rendering applications because AO produces more realistic ambient lighting, which is achieved by calculating the brightness of certain screen parts based on objects’ geometry. However, the baseline computation of AO algorithm is time-consuming, which limits its application for real-t...
Article
Vectorizing line drawing is necessary for the digital workflows of 2D animation and engineering design. But it is challenging due to the ambiguity of topology, especially at junctions. Existing vectorization methods either suffer from low accuracy or cannot deal with high‐resolution images. To deal with a variety of challenging containing different...
Article
Top-down, goal-driven visual saliency exerts a huge influence on the human visual system for performing visual tasks. Text generations, like visual question answering (VQA) and visual question generation (VQG), have intrinsic connections with top-down saliency, which is usually involved in both VQA and VQG processes in an unsupervised manner. Howev...
Article
Human age has been treated as an important biometric trait in many practical applications. In this paper, we propose an Attribute-Region Association Network (ARAN) to tackle the challenging age estimation problem. Instead of performing prediction from a global perspective, we delve into the relationship between face attributes and regions. First, t...
Article
Full-text available
Binocular tone mapping is studied in the previous works to generate a fusible pair of LDR images in order to convey more visual content than one single LDR image. However, the existing methods are all based on monocular tone mapping operators. It greatly restricts the preservation of local details and global contrast in a binocular LDR pair. In thi...
Article
Full-text available
We propose a novel deep example‐based image colourization method called dense encoding pyramid network. In our study, we define the colourization as a multinomial classification problem. Given a greyscale image and a reference image, the proposed network leverages large‐scale data and then predicts colours by analysing the colour distribution of th...
Article
Full-text available
Accurate and timely traffic flow forecasting is essential for many intelligent transportation systems. However, it is quite challenging to develop an efficient and robust forecasting model due to the inherent randomness and large variations of traffic flow. Over the past two decades, a variety of traffic flow forecasting models have been proposed....
Conference Paper
In this paper, we present a novel unsupervised learning method for pixelization. Due to the difficulty in creating pixel art, preparing the paired training data for supervised learning is impractical. Instead, we propose an unsupervised learning framework to circumvent such difficulty. We leverage the dual nature of the pixelization and depixelizat...
Article
Full-text available
Shape matching plays an important role in various computer vision and graphics applications such as shape retrieval, object detection, image editing, image retrieval, etc. However, detecting shapes in cluttered images is still quite challenging due to the incomplete edges and changing perspective. In this paper, we propose a novel approach that can...
Article
Accurate and timely traffic flow forecasting is critical for the successful deployment of intelligent transportation systems. However, it is quite challenging to develop an efficient and robust forecasting model due to the inherent randomness and large variations of traffic flow. Recently, the stacked autoencoder has been proven promising for traff...
Article
This paper tackles a challenging 2D collage generation problem, focusing on shapes: we aim to fill a given region by packing irregular and reasonably-sized shapes with minimized gaps and overlaps. To achieve this nontrivial problem, we first have to analyze the boundary of individual shapes and then couple the shapes with partially-matched boundary...
Conference Paper
This paper tackles a challenging 2D collage generation problem, focusing on shapes: we aim to fill a given region by packing irregular and reasonably-sized shapes with minimized gaps and overlaps. To achieve this nontrivial problem, we first have to analyze the boundary of individual shapes and then couple the shapes with partially-matched boundary...

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