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Cheng Lu

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22 papers

2 author rows

EAAI Journal 2025 Journal Article

Enhanced feedback analysis of vertical load reliability parameters for airplane landing gear using an improved generative adversarial network and explainable artificial intelligence techniques

Weihuang Pan
Yunwen Feng
Cheng Lu
Jiaqi Liu
Jingcui Liang

Effective feedback analysis of critical equipment data is essential for improving performance and optimizing design parameters in aviation systems. This study presents a novel framework that integrates an improved generative adversarial network (GAN) with explainable artificial intelligence techniques (XAI) to evaluate the reliability of the vertical load for airplane landing gear. By utilizing limited data from the Quick Access Recorder (QAR), the improved GAN generates extensive synthetic data to expand the dataset and strengthen the analysis. Each parameter's importance and influence on vertical load reliability are then evaluated through the Shapley Additive Explanations (SHAP) method, a key approach in XAI. Validation using landing gear data from a typical civil airplane demonstrates the effectiveness of this method and confirms the viability of explainable artificial intelligence for parametric feedback analysis. The results highlight the impact of each parameter on vertical load reliability, providing valuable insights to support enhanced design and operational efficiency of landing gear.

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EAAI Journal 2025 Journal Article

Few-shot augmentation based on variational auto-generative adversarial network with moving losses: Application to the variable stiffness prediction in composites

Zhicen Song
Yunwen Feng
Cheng Lu
Jiaqi Liu

The dataset of carbon fiber reinforced plastics (CFRP) materials presents characteristics of high-dimensional, low-rank, and sparse, which pose difficulties in the combination of mechanical modeling. In this paper, a Variational Auto-Generative Adversarial Network (VAGAN) with moving losses is proposed as a data augmentation method, which extends the size of the CFRP dataset covering components, processes, elasticity, and strengths factors, and increases the information conveyed in the surrogate modeling. A compression strength prediction model for CFRP laminates was constructed by combining the component, process, and mechanical tensor with a neural network optimized by the search algorithm. Combined with the data augmentation strategy, not only was the amount of data expanded, but the prediction accuracy was also significantly improved. The allowable value of compression strength is analyzed and calculated by the predicted values, which brings direct benefits in simplifying the test.

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ICLR Conference 2025 Conference Paper

Simplifying, Stabilizing and Scaling Continuous-time Consistency Models

Cheng Lu
Yang Song

Consistency models (CMs) are a powerful class of diffusion-based generative models optimized for fast sampling. Most existing CMs are trained using discretized timesteps, which introduce additional hyperparameters and are prone to discretization errors. While continuous-time formulations can mitigate these issues, their success has been limited by training instability. To address this, we propose a simplified theoretical framework that unifies previous parameterizations of diffusion models and CMs, identifying the root causes of instability. Based on this analysis, we introduce key improvements in diffusion process parameterization, network architecture, and training objectives. These changes enable us to train continuous-time CMs at an unprecedented scale, reaching 1.5B parameters on ImageNet 512×512. Our proposed training algorithm, using only two sampling steps, achieves FID scores of 2.06 on CIFAR-10, 1.48 on ImageNet 64×64, and 1.88 on ImageNet 512×512, narrowing the gap in FID scores with the best existing diffusion models to within 10\%.

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EAAI Journal 2025 Journal Article

Weakly supervised histopathology tissue semantic segmentation with multi-scale voting and online noise suppression

Xipeng Pan
Hualong Zhang
Huahu Deng
Huadeng Wang
Lingqiao Li
Zhenbing Liu
Lin Wang
Yajun An

The development of an Artificial Intelligence (AI) assisted tissue segmentation method of digital pathology images is critical for cancer diagnosis and prognosis. Excellent performance has been achieved with the current fully supervised segmentation approach, which relies on a huge number of annotated data. However, drawing dense pixel-level annotations on the giga-pixel whole slide image (WSI) is extremely time-consuming and labor-intensive. To this end, we propose a tissue segmentation method using only patch-level classification labels to reduce such annotation burden and significantly improve the quality of the pseudo-masks. We introduce a framework with two phases of classification and segmentation. In the classification phase, we propose a multi-scale voting method on the Class Activation Map (CAM) based model to obtain more stable pseudo masks. In the segmentation phase, an Online Noise Suppression Strategy (ONSS) is proposed to encourage the model to focus on more reliable signals in the pseudo mask rather than noisy signals. Extensive experiments on two weakly supervised pathology image tissue segmentation datasets Lung Adenocarcinoma (LUAD-HistoSeg) and Breast Cancer Semantic Segmentation (BCSS-WSSS) demonstrate our model outperforms state-of-the-art weakly-supervised semantic segmentation (WSSS) methods using patch-level labels. Furthermore, our method exhibits superior generalization ability compared to other models, and demonstrates promising adaptation performance on unseen domains with only small amounts of data.

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JBHI Journal 2024 Journal Article

CroMAM: A Cross-Magnification Attention Feature Fusion Model for Predicting Genetic Status and Survival of Gliomas Using Histological Images

Jisen Guo
Peng Xu
Yuankui Wu
Yunyun Tao
Chu Han
Jiatai Lin
Ke Zhao
Zaiyi Liu

Predicting the gene mutation status in whole slide images (WSIs) is crucial for the clinical treatment, cancer management, and research of gliomas. With advancements in CNN and Transformer algorithms, several promising models have been proposed. However, existing studies have paid little attention on fusing multi-magnification information, and the model requires processing all patches from a whole slide image. In this paper, we propose a cross-magnification attention model called CroMAM for predicting the genetic status and survival of gliomas. The CroMAM first utilizes a systematic patch extraction module to sample a subset of representative patches for downstream analysis. Next, the CroMAM applies Swin Transformer to extract local and global features from patches at different magnifications, followed by acquiring high-level features and dependencies among single-magnification patches through the application of a Vision Transformer. Subsequently, the CroMAM exchanges the integrated feature representations of different magnifications and encourage the integrated feature representations to learn the discriminative information from other magnification. Additionally, we design a cross-magnification attention analysis method to examine the effect of cross-magnification attention quantitatively and qualitatively which increases the model's explainability. To validate the performance of the model, we compare the proposed model with other multi-magnification feature fusion models on three tasks in two datasets. Extensive experiments demonstrate that the proposed model achieves state-of-the-art performance in predicting the genetic status and survival of gliomas.

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AAAI Conference 2024 Conference Paper

Privileged Prior Information Distillation for Image Matting

Cheng Lyu
Jiake Xie
Bo Xu
Cheng Lu
Han Huang
Xin Huang
Ming Wu
Chuang Zhang

Performance of trimap-free image matting methods is limited when trying to decouple the deterministic and undetermined regions, especially in the scenes where foregrounds are semantically ambiguous, chromaless, or high transmittance. In this paper, we propose a novel framework named Privileged Prior Information Distillation for Image Matting (PPID-IM) that can effectively transfer privileged prior environment-aware information to improve the performance of trimap-free students in solving hard foregrounds. The prior information of trimap regulates only the teacher model during the training stage, while not being fed into the student network during actual inference. To achieve effective privileged cross-modality (i.e. trimap and RGB) information distillation, we introduce a Cross-Level Semantic Distillation (CLSD) module that reinforces the students with more knowledgeable semantic representations and environment-aware information. We also propose an Attention-Guided Local Distillation module that efficiently transfers privileged local attributes from the trimap-based teacher to trimap-free students for the guidance of local-region optimization. Extensive experiments demonstrate the effectiveness and superiority of our PPID on image matting. The code will be released soon.

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JBHI Journal 2024 Journal Article

Protecting Prostate Cancer Classification From Rectal Artifacts via Targeted Adversarial Training

Lei Hu
Dawei Zhou
Jiahua Xu
Cheng Lu
Chu Han
Zhenwei Shi
Qikui Zhu
Xinbo Gao

Magnetic resonance imaging (MRI)-based deep neural networks (DNN) have been widely developed to perform prostate cancer (PCa) classification. However, in real-world clinical situations, prostate MRIs can be easily impacted by rectal artifacts, which have been found to lead to incorrect PCa classification. Existing DNN-based methods typically do not consider the interference of rectal artifacts on PCa classification, and do not design specific strategy to address this problem. In this study, we proposed a novel Targeted adversarial training with Proprietary Adversarial Samples (TPAS) strategy to defend the PCa classification model against the influence of rectal artifacts. Specifically, based on clinical prior knowledge, we generated proprietary adversarial samples with rectal artifact-pattern adversarial noise, which can severely mislead PCa classification models optimized by the ordinary training strategy. We then jointly exploited the generated proprietary adversarial samples and original samples to train the models. To demonstrate the effectiveness of our strategy, we conducted analytical experiments on multiple PCa classification models. Compared with ordinary training strategy, TPAS can effectively improve the single- and multi-parametric PCa classification at patient, slice and lesion level, and bring substantial gains to recent advanced models. In conclusion, TPAS strategy can be identified as a valuable way to mitigate the influence of rectal artifacts on deep learning models for PCa classification.

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AAAI Conference 2023 Conference Paper

CMNet: Contrastive Magnification Network for Micro-Expression Recognition

Mengting Wei
Xingxun Jiang
Wenming Zheng
Yuan Zong
Cheng Lu
Jiateng Liu

Micro-Expression Recognition (MER) is challenging because the Micro-Expressions' (ME) motion is too weak to distinguish. This hurdle can be tackled by enhancing intensity for a more accurate acquisition of movements. However, existing magnification strategies tend to use the features of facial images that include not only intensity clues as intensity features, leading to the intensity representation deficient of credibility. In addition, the intensity variation over time, which is crucial for encoding movements, is also neglected. To this end, we provide a reliable scheme to extract intensity clues while considering their variation on the time scale. First, we devise an Intensity Distillation (ID) loss to acquire the intensity clues by contrasting the difference between frames, given that the difference in the same video lies only in the intensity. Then, the intensity clues are calibrated to follow the trend of the original video. Specifically, due to the lack of truth intensity annotation of the original video, we build the intensity tendency by setting each intensity vacancy an uncertain value, which guides the extracted intensity clues to converge towards this trend rather some fixed values. A Wilcoxon rank sum test (Wrst) method is enforced to implement the calibration. Experimental results on three public ME databases i.e. CASME II, SAMM, and SMIC-HS validate the superiority against state-of-the-art methods.

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AAAI Conference 2023 Conference Paper

Domain Adaptation with Adversarial Training on Penultimate Activations

Tao Sun
Cheng Lu
Haibin Ling

Enhancing model prediction confidence on target data is an important objective in Unsupervised Domain Adaptation (UDA). In this paper, we explore adversarial training on penultimate activations, i.e., input features of the final linear classification layer. We show that this strategy is more efficient and better correlated with the objective of boosting prediction confidence than adversarial training on input images or intermediate features, as used in previous works. Furthermore, with activation normalization commonly used in domain adaptation to reduce domain gap, we derive two variants and systematically analyze the effects of normalization on our adversarial training. This is illustrated both in theory and through empirical analysis on real adaptation tasks. Extensive experiments are conducted on popular UDA benchmarks under both standard setting and source-data free setting. The results validate that our method achieves the best scores against previous arts. Code is available at https://github.com/tsun/APA.

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NeurIPS Conference 2023 Conference Paper

DPM-Solver-v3: Improved Diffusion ODE Solver with Empirical Model Statistics

Kaiwen Zheng
Cheng Lu
Jianfei Chen
Jun Zhu

Diffusion probabilistic models (DPMs) have exhibited excellent performance for high-fidelity image generation while suffering from inefficient sampling. Recent works accelerate the sampling procedure by proposing fast ODE solvers that leverage the specific ODE form of DPMs. However, they highly rely on specific parameterization during inference (such as noise/data prediction), which might not be the optimal choice. In this work, we propose a novel formulation towards the optimal parameterization during sampling that minimizes the first-order discretization error of the ODE solution. Based on such formulation, we propose \textit{DPM-Solver-v3}, a new fast ODE solver for DPMs by introducing several coefficients efficiently computed on the pretrained model, which we call \textit{empirical model statistics}. We further incorporate multistep methods and a predictor-corrector framework, and propose some techniques for improving sample quality at small numbers of function evaluations (NFE) or large guidance scales. Experiments show that DPM-Solver-v3 achieves consistently better or comparable performance in both unconditional and conditional sampling with both pixel-space and latent-space DPMs, especially in 5$\sim$10 NFEs. We achieve FIDs of 12. 21 (5 NFE), 2. 51 (10 NFE) on unconditional CIFAR10, and MSE of 0. 55 (5 NFE, 7. 5 guidance scale) on Stable Diffusion, bringing a speed-up of 15\%$\sim$30\% compared to previous state-of-the-art training-free methods. Code is available at \url{https: //github. com/thu-ml/DPM-Solver-v3}.

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JBHI Journal 2023 Journal Article

EEG-Based Parkinson's Disease Recognition via Attention-Based Sparse Graph Convolutional Neural Network

Hongli Chang
Bo Liu
Yuan Zong
Cheng Lu
Xuenan Wang

Parkinson's disease (PD) is a complicated neurological ailment that affects both the physical and mental wellness of elderly individuals which makes it problematic to diagnose in its initial stages. Electroencephalogram (EEG) promises to be an efficient and cost-effective method for promptly detecting cognitive impairment in PD. Nevertheless, prevailing diagnostic practices utilizing EEG features have failed to examine the functional connectivity among EEG channels and the response of associated brain areas causing an unsatisfactory level of precision. Here, we construct an attention-based sparse graph convolutional neural network (ASGCNN) for diagnosing PD. Our ASGCNN model uses a graph structure to represent channel relationships, the attention mechanism for selecting channels, and the L1 norm to capture channel sparsity. We conduct extensive experiments on the publicly available PD auditory oddball dataset, which consists of 24 PD patients (under ON/OFF drug status) and 24 matched controls, to validate the effectiveness of our method. Our results show that the proposed method provides better results compared to the publicly available baselines. The achieved scores for Recall, Precision, F1-score, Accuracy and Kappa measures are 90. 36%, 88. 43%, 88. 41%, 87. 67%, and 75. 24%, respectively. Our study reveals that the frontal and temporal lobes show significant differences between PD patients and healthy individuals. In addition, EEG features extracted by ASGCNN demonstrate significant asymmetry in the frontal lobe among PD patients. These findings can offer a basis for the establishment of a clinical system for intelligent diagnosis of PD by using auditory cognitive impairment features.

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NeurIPS Conference 2023 Conference Paper

Gaussian Mixture Solvers for Diffusion Models

Hanzhong Guo
Cheng Lu
Fan Bao
Tianyu Pang
Shuicheng Yan
Chao Du
Chongxuan Li

Recently, diffusion models have achieved great success in generative tasks. Sampling from diffusion models is equivalent to solving the reverse diffusion stochastic differential equations (SDEs) or the corresponding probability flow ordinary differential equations (ODEs). In comparison, SDE-based solvers can generate samples of higher quality and are suited for image translation tasks like stroke-based synthesis. During inference, however, existing SDE-based solvers are severely constrained by the efficiency-effectiveness dilemma. Our investigation suggests that this is because the Gaussian assumption in the reverse transition kernel is frequently violated (even in the case of simple mixture data) given a limited number of discretization steps. To overcome this limitation, we introduce a novel class of SDE-based solvers called \emph{Gaussian Mixture Solvers (GMS)} for diffusion models. Our solver estimates the first three-order moments and optimizes the parameters of a Gaussian mixture transition kernel using generalized methods of moments in each step during sampling. Empirically, our solver outperforms numerous SDE-based solvers in terms of sample quality in image generation and stroke-based synthesis in various diffusion models, which validates the motivation and effectiveness of GMS. Our code is available at https: //github. com/Guohanzhong/GMS.

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NeurIPS Conference 2023 Conference Paper

On Calibrating Diffusion Probabilistic Models

Tianyu Pang
Cheng Lu
Chao Du
Min Lin
Shuicheng Yan
Zhijie Deng

Recently, diffusion probabilistic models (DPMs) have achieved promising results in diverse generative tasks. A typical DPM framework includes a forward process that gradually diffuses the data distribution and a reverse process that recovers the data distribution from time-dependent data scores. In this work, we observe that the stochastic reverse process of data scores is a martingale, from which concentration bounds and the optional stopping theorem for data scores can be derived. Then, we discover a simple way for calibrating an arbitrary pretrained DPM, with which the score matching loss can be reduced and the lower bounds of model likelihood can consequently be increased. We provide general calibration guidelines under various model parametrizations. Our calibration method is performed only once and the resulting models can be used repeatedly for sampling. We conduct experiments on multiple datasets to empirically validate our proposal. Our code is available at https: //github. com/thudzj/Calibrated-DPMs.

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NeurIPS Conference 2023 Conference Paper

ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation

Zhengyi Wang
Cheng Lu
Yikai Wang
Fan Bao
Chongxuan Li
Hang Su
Jun Zhu

Score distillation sampling (SDS) has shown great promise in text-to-3D generation by distilling pretrained large-scale text-to-image diffusion models, but suffers from over-saturation, over-smoothing, and low-diversity problems. In this work, we propose to model the 3D parameter as a random variable instead of a constant as in SDS and present *variational score distillation* (VSD), a principled particle-based variational framework to explain and address the aforementioned issues in text-to-3D generation. We show that SDS is a special case of VSD and leads to poor samples with both small and large CFG weights. In comparison, VSD works well with various CFG weights as ancestral sampling from diffusion models and simultaneously improves the diversity and sample quality with a common CFG weight (i. e. , 7. 5). We further present various improvements in the design space for text-to-3D such as distillation time schedule and density initialization, which are orthogonal to the distillation algorithm yet not well explored. Our overall approach, dubbed *ProlificDreamer*, can generate high rendering resolution (i. e. , 512$\times$512) and high-fidelity NeRF with rich structure and complex effects (e. g. , smoke and drops). Further, initialized from NeRF, meshes fine-tuned by VSD are meticulously detailed and photo-realistic.

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NeurIPS Conference 2022 Conference Paper

DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps

Cheng Lu
Yuhao Zhou
Fan Bao
Jianfei Chen
Chongxuan Li
Jun Zhu

Diffusion probabilistic models (DPMs) are emerging powerful generative models. Despite their high-quality generation performance, DPMs still suffer from their slow sampling as they generally need hundreds or thousands of sequential function evaluations (steps) of large neural networks to draw a sample. Sampling from DPMs can be viewed alternatively as solving the corresponding diffusion ordinary differential equations (ODEs). In this work, we propose an exact formulation of the solution of diffusion ODEs. The formulation analytically computes the linear part of the solution, rather than leaving all terms to black-box ODE solvers as adopted in previous works. By applying change-of-variable, the solution can be equivalently simplified to an exponentially weighted integral of the neural network. Based on our formulation, we propose DPM-Solver, a fast dedicated high-order solver for diffusion ODEs with the convergence order guarantee. DPM-Solver is suitable for both discrete-time and continuous-time DPMs without any further training. Experimental results show that DPM-Solver can generate high-quality samples in only 10 to 20 function evaluations on various datasets. We achieve 4. 70 FID in 10 function evaluations and 2. 87 FID in 20 function evaluations on the CIFAR10 dataset, and a 4~16x speedup compared with previous state-of-the-art training-free samplers on various datasets.

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JBHI Journal 2021 Journal Article

Integrated Clinical and CT Based Artificial Intelligence Nomogram for Predicting Severity and Need for Ventilator Support in COVID-19 Patients: A Multi-Site Study

Amogh Hiremath
Kaustav Bera
Lei Yuan
Pranjal Vaidya
Mehdi Alilou
Jennifer Furin
Keith Armitage
Robert Gilkeson

Almost 25% of COVID-19 patients end up in ICU needing critical mechanical ventilation support. There is currently no validated objective way to predict which patients will end up needing ventilator support, when the disease is mild and not progressed. N = 869 patients from two sites (D 1: N = 822, D 2: N = 47) with baseline clinical characteristics and chest CT scans were considered for this study. The entire dataset was randomly divided into 70% training, D 1 train (N = 606) and 30% test-set (D test: D 1 test (N = 216) + D 2 (N = 47)). An expert radiologist delineated ground-glass-opacities (GGOs) and consolidation regions on a subset of D 1 train, (D 1 train_sub, N = 88). These regions were automatically segmented and used along with their corresponding CT volumes to train an imaging AI predictor (AIP) on D 1 train to predict the need of mechanical ventilators for COVID-19 patients. Finally, top five prognostic clinical factors selected using univariate analysis were integrated with AIP to construct an integrated clinical and AI imaging nomogram (ClAIN). Univariate analysis identified lactate dehydrogenase, prothrombin time, aspartate aminotransferase, %lymphocytes, albumin as top five prognostic clinical features. AIP yielded an AUC of 0. 81 on D test and was independently prognostic irrespective of other clinical parameters on multivariable analysis (p test. ClAIN outperformed AIP in predicting which COVID-19 patients ended up needing a ventilator. Our results across multiple sites suggest that ClAIN could help identify COVID-19 with severe disease more precisely and likely to end up on a life-saving mechanical ventilation.

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EAAI Journal 2020 Journal Article

Fault diagnosis model based on Granular Computing and Echo State Network

Cheng Lu
Peng Xu
Lin-hu Cong

In order to improve the efficiency and accuracy of electronic equipment fault diagnosis, a fault diagnosis model based on Granular Computing and Echo State Network (ESN) is proposed. Firstly, the attribute reduction of test index is carried out based on granular computing model. An attribute distinguishing ability index is defined based on attribute value influence degree. As the basis of similarity measure, a number of attribute granules of similar distinguish are obtained through affinity propagation clustering algorithm, then fault attribute reduction was completed by selecting clustering center attributes. In the stage of fault identification by ESN, in order to improve the dynamic adaptability of ESN reservoir to samples, Bienenstock–Cooper–Munro(BCM) rule is introduced into the reservoir construction to train the connection weight matrix. Meanwhile, the L 1 ∕ 2 -norm penalty term is added to the objective function in order to improve the sparsification efficiency, and a smoothing L 1 ∕ 2 -norm regularization term is used to overcome the iterative numerical oscillation problem, the model is solved by using the half threshold iteration method at last. The effectiveness and superiority of the proposed method are verified by a fault diagnosis example of terminal guidance radar signal processing module.

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NeurIPS Conference 2019 Conference Paper

Staying up to Date with Online Content Changes Using Reinforcement Learning for Scheduling

Andrey Kolobov
Yuval Peres
Cheng Lu
Eric Horvitz

From traditional Web search engines to virtual assistants and Web accelerators, services that rely on online information need to continually keep track of remote content changes by explicitly requesting content updates from remote sources (e. g. , web pages). We propose a novel optimization objective for this setting that has several practically desirable properties, and efficient algorithms for it with optimality guarantees even in the face of mixed content change observability and initially unknown change model parameters. Experiments on 18. 5M URLs crawled daily for 14 weeks show significant advantages of this approach over prior art.

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JBHI Journal 2017 Journal Article

Automatic Nuclei Detection Based on Generalized Laplacian of Gaussian Filters

Hongming Xu
Cheng Lu
Richard Berendt
Naresh Jha
Mrinal Mandal

Efficient and accurate detection of cell nuclei is an important step toward automatic analysis in histopathology. In this work, we present an automatic technique based on generalized Laplacian of Gaussian (gLoG) filter for nuclei detection in digitized histological images. The proposed technique first generates a bank of gLoG kernels with different scales and orientations and then performs convolution between directional gLoG kernels and the candidate image to obtain a set of response maps. The local maxima of response maps are detected and clustered into different groups by mean-shift algorithm based on their geometrical closeness. The point which has the maximum response in each group is finally selected as the nucleus seed. Experimental results on two datasets show that the proposed technique provides a superior performance in nuclei detection compared to existing techniques.

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JBHI Journal 2014 Journal Article

An Efficient Technique for Nuclei Segmentation Based on Ellipse Descriptor Analysis and Improved Seed Detection Algorithm

Hongming Xu
Cheng Lu
Mrinal Mandal

In this paper, we propose an efficient method for segmenting cell nuclei in the skin histopathological images. The proposed technique consists of four modules. First, it separates the nuclei regions from the background with an adaptive threshold technique. Next, an elliptical descriptor is used to detect the isolated nuclei with elliptical shapes. This descriptor classifies the nuclei regions based on two ellipticity parameters. Nuclei clumps and nuclei with irregular shapes are then localized by an improved seed detection technique based on voting in the eroded nuclei regions. Finally, undivided nuclei regions are segmented by a marked watershed algorithm. Experimental results on 114 different image patches indicate that the proposed technique provides a superior performance in nuclei detection and segmentation.

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JBHI Journal 2014 Journal Article

Toward Automatic Mitotic Cell Detection and Segmentation in Multispectral Histopathological Images

Cheng Lu
Mrinal Mandal

The count of mitotic cells is a critical factor in most cancer grading systems. Extracting the mitotic cell from the histopathological image is a very challenging task. In this paper, we propose an efficient technique for detecting and segmenting the mitotic cells in the high-resolution multispectral image. The proposed technique consists of three main modules: discriminative image generation, mitotic cell candidate detection and segmentation, and mitotic cell candidate classification. In the first module, a discriminative image is obtained by linear discriminant analysis using ten different spectral band images. A set of mitotic cell candidate regions is then detected and segmented by the Bayesian modeling and local-region threshold method. In the third module, a 226 dimension feature is extracted from the mitotic cell candidates and their surrounding regions. An imbalanced classification framework is then applied to perform the classification for the mitotic cell candidates in order to detect the real mitotic cells. The proposed technique has been evaluated on a publicly available dataset of 35 × 10 multispectral images, in which 224 mitotic cells are manually labeled by experts. The proposed technique is able to provide superior performance compared to the existing technique, 81. 5% sensitivity rate and 33. 9% precision rate in terms of detection performance, and 89. 3% sensitivity rate and 87. 5% precision rate in terms of segmentation performance.

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JBHI Journal 2013 Journal Article

Automated Segmentation of the Melanocytes in Skin Histopathological Images

Cheng Lu
Muhammad Mahmood
Naresh Jha
Mrinal Mandal

In the diagnosis of skin melanoma by analyzing histopathological images, the detection of the melanocytes in the epidermis area is an important step. However, the detection of melanocytes in the epidermis area is difficult because other keratinocytes that are very similar to the melanocytes are also present. This paper proposes a novel computer-aided technique for segmentation of the melanocytes in the skin histopathological images. In order to reduce the local intensity variant, a mean-shift algorithm is applied for the initial segmentation of the image. A local region recursive segmentation algorithm is then proposed to filter out the candidate nuclei regions based on the domain prior knowledge. To distinguish the melanocytes from other keratinocytes in the epidermis area, a novel descriptor, named local double ellipse descriptor (LDED), is proposed to measure the local features of the candidate regions. The LDED uses two parameters: region ellipticity and local pattern characteristics to distinguish the melanocytes from the candidate nuclei regions. Experimental results on 28 different histopathological images of skin tissue with different zooming factors show that the proposed technique provides a superior performance.

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