Protein-ligand binding affinity prediction constitutes an important task in computational drug discovery, where geometric invariance is a key property. Since binding affinity is inherently independent of coordinate systems, prediction outcomes should be invariant with respect to molecular orientations. Current methods incorporating geometric principles often enforce invariance using specialized architectural components, but these may fail to maintain strict equivariance throughout computation. Additionally, existing multi-modal frameworks typically process heterogeneous molecular modalities separately before fusion, potentially compromising geometric consistency. We introduce EPLA (Equivariant Protein-Ligand Affinity network), which addresses these limitations through: (1) a representation grounded in geometric algebra, preserving SE(3) equivariance via algebraic operations; (2) a multi-stream attention mechanism that simultaneously processes geometric and chemical information under equivariance constraints; and (3) a hierarchical cross-attention architecture that integrates multiple molecular representations in an equivariant manner. Evaluation on the PDBbind dataset demonstrates that EPLA achieves improved prediction accuracy (RMSE of$\mathbf{1. 1 1 5}$compared to$\mathbf{1. 1 5 1}$for the next best method) while maintaining consistent performance across molecular orientations, with performance variations of$\mathbf{0. 0 9 \%}$under SE(3) transformations compared to 2.09-5.71% in existing methods. Ablation studies confirm the contributions of both equivariant representation and multi-modal components to these improvements.

Missing modalities pose a major issue in Alzheimer's Disease (AD) diagnosis, as many subjects lack full imaging data due to cost and clinical constraints. While multi-modal learning leverages complementary information, most existing methods train only on complete data, ignoring the large proportion of incomplete samples in real-world datasets like ADNI. This reduces the effective training set and limits the full use of valuable medical data. While some methods incorporate incomplete samples, they fail to effectively address inter-modal feature alignment and knowledge transfer challenges under high missing rates. To address this, we propose a Prototype-Guided Adaptive Distillation (PGAD) framework that directly incorporates incomplete multimodal data into training. PGAD enhances missing modality representations through prototype matching and balances learning with a dynamic sampling strategy. We validate PGAD on the ADNI dataset with varying missing rates$(20 \%, 50 \%$, and 70 %) and demonstrate that it significantly outperforms state-of-the-art approaches. Ablation studies confirm the effectiveness of prototype matching and adaptive sampling, highlighting the potential of our framework for robust and scalable AD diagnosis in real-world clinical settings.

Mixed supervision strikes a balance between performance and annotation efficiency by using a small subset of fully pixel-annotated data alongside sparsely annotated data. The key challenge lies in effectively utilizing sparse labels, as conventional pseudo-labeling relies mainly on prediction probabilities or lowlevel similarities, neglecting intra-class compactness and interclass separability. To address this issue, we proposed ProtoD2C2, a novel prototype-based framework for OCT fluid segmentation under mixed supervision, which includes three key components: (1) dual-supervision scheme that combines direct supervision with prototype-generated pseudo-labels; (2) dual-consistency at both the prediction level and prototype level; (3) hierarchical dual-contrastive learning strategy that first enforces anatomical structure priors, then promotes fine-grained discrimination among fluid subtypes. To the best of our knowledge, we are the first to introduce prototype learning for OCT fluid segmentation under mixed supervision. We evaluated our method on two public OCT fluid segmentation datasets RETOUCH and AROI, demonstrating superior performance compared to state-of-theart annotation-efficient segmentation approaches.

A comprehensive measurement of choroidal thickness is essential for diagnosing various ocular conditions. Automated measurement encounters difficulties due to the high proportion of fuzzy boundaries in choroidal segmentation. In case of limited annotated data, large amounts of unlabeled data hinder the effective learning of boundary features makes it a challenge to extract features from fuzzy boundaries. To address this issue, we propose a semi-supervised segmentation framework based on contrastive learning, called Attention-based Refinement Contrastive Semi-Supervised Learning (ARC-SSL). This framework is designed to utilize labeled data to effectively extract boundary features from unlabeled data. Furthermore, we introduce an innovative attention-based strategy for refining the assignment of samples in contrastive learning. Experimental results demonstrate that our ARC-SSL outperforms state-of-the-art models on both public and private datasets.

Glaucoma is a leading cause of irreversible blindness worldwide, requiring early screening for timely treatment to maintain vision and quality of life. The automated multi-task screening approaches conduct glaucoma classification, as well as optic cup and optic disc segmentation simultaneously, enhancing interpretability by providing probabilities and essential morphological biomarkers. However, existing methods struggle with generalization, as they fail to adapt to the domain shift encountered across different datasets on both tasks in parallel. To address this issue, we propose a novel mutual multi-task network with unsupervised domain adaptation (MMUDA) to realize cross-domain accurate glaucoma screening. We devise a multi-scale classification branch for glaucoma and an adversarial segmentation branch for optic cup and optic disk, ensuring global domain alignment. Additionally, we design a multi-task mutual pseudo supervision (MMPS) training strategy for aligning category-wise domain representation in both tasks. Furthermore, our network presents the dual dimensional self-attention (DDSA) mechanism to enhance cross-domain relationship understanding within mixed-domain mini-batches. Extensive experiments on three public datasets prove that MMUDA outperforms the unsupervised domain adaptation methods in glaucoma screening. Our source code is available at https://github.com/AllenYT/MMUDA.

Precisely segmenting the organs at risk (OARs) in computed tomography (CT) plays an important role in radiotherapy’s treatment planning, aiding in the protection of critical tissues during irradiation. Renowned deep convolutional neural networks (DCNNs) and prevailing transformer-based architectures are widely utilized to accomplish the segmentation task, showcasing advantages in capturing local and contextual characteristics. Graph convolutional networks (GCNs) are another specialized model designed for processing the nongrid dataset, e.g., citation relationship. The DCNNs and GCNs are considered as two distinct models applicable to the grid and nongrid datasets, respectively. Motivated by the recently developed dynamic-channel GCN (DCGCN) that attempts to leverage the graph structure to enhance the feature extracted by the DCNNs, this paper proposes a novel architecture termed adaptive sparse GCN (ASGCN) to mitigate the inherent limitations in DCGCN from the aspect of node’s representation and adjacency matrix’s construction. For the node’s representation, the global average pooling used in the DCGCN is replaced by the learning mechanism to accommodate the segmentation task. For the adjacency matrix, an adaptive regularization strategy is leveraged to penalize the coefficient in the adjacency matrix, resulting in a sparse one that can better exploit the relationships between nodes. Rigorous experiments on multiple OARs’ segmentation tasks of the head and neck demonstrate that the proposed ASGCN can effectively improve the segmentation accuracy. Comparison between the proposed method and other prevalent architectures further confirms the superiority of the ASGCN.

In colorectal cancer (CRC), accurately predicting lymph node metastasis (LNM) contributes to developing appropriate treatment plans and serves as the key to long‐term survival of patients. In the clinical settings, preoperative LNM diagnosis in CRC predominantly depends on computed tomography (CT). Nevertheless, lymph nodes are small in size and difficult to identify on 3D CT scans, and CT‐based diagnosis of metastatic lymph nodes is prone to a significant misdiagnosis rate and lacks consistency across clinicians. Currently, there is no automatic system available for LNM prediction in CRC via 3D CT scans. In addition, existing deep learning‐ (DL‐) based lymph node detection models present low detection accuracy and high false‐positive rates, and most existing DL‐based lymph node metastasis prediction models mainly use tumor area characteristics but fail to adequately utilize lymph node information, thus not yielding satisfactory results. To tackle these issues, we propose an intelligent diagnosis system for this challenging task, mainly including a lymph node detection (LND) model and a lymph node metastasis prediction (LNMP) model. In detail, the LND model utilizes an encoder‐decoder network to detect lymph nodes, and the LNMP model employs an innovative attention‐based multiple instance learning (MIL) network. An instance‐level self‐attention feature enhancement module is designed to extract and augment lymph node features as a bag of instances. Furthermore, a bag‐level MIL prediction module is employed to extract instance features and create a bag representation for the ultimate LNM prediction. As far as we know, the proposed intelligent system represents the pioneering method for addressing this complex clinical challenge. In experiments, our proposed intelligent system achieves the AUC of 75.4% and the accuracy of 73.9%, showcasing a significant enhancement compared to physicians specialising in CRC and highlighting its strong clinical applicability. The accessible code can be found at https://github.com/SCU-MI/IS-LNM .

The deep neural network, based on the backpropagation learning algorithm, has achieved tremendous success. However, the backpropagation algorithm is consistently considered biologically implausible. Many efforts have recently been made to address these biological implausibility issues, nevertheless, these methods are tailored to discrete neural network structures. Continuous neural networks are crucial for investigating novel neural network models with more biologically dynamic characteristics and for interpretability of large language models. The neural memory ordinary differential equation (nmODE) is a recently proposed continuous neural network model that exhibits several intriguing properties. In this study, we present a forward-learning algorithm, called nmForwardLA, for nmODE. This algorithm boasts lower computational dimensions and greater efficiency. Compared with the other learning algorithms, experimental results on MNIST, CIFAR10, and CIFAR100 demonstrate its potency.

Incomplete multiview clustering (IMVC) aims to reveal the underlying structure of incomplete multiview data by partitioning data samples into clusters. Several graph-based methods exhibit a strong ability to explore high-order information among multiple views using low-rank tensor learning. However, spectral embedding fusion of multiple views is ignored in low-rank tensor learning. In addition, addressing missing instances or features is still an intractable problem for most existing IMVC methods. In this paper, we present a unified spectral embedding tensor learning (USETL) framework that integrates the spectral embedding fusion of multiple similarity graphs and spectral embedding tensor learning for IMVC. To remove redundant information from the original incomplete multiview data, spectral embedding fusion is performed by introducing spectral rotations at two different data levels, i.e., the spectral embedding feature level and the clustering indicator level. The aim of introducing spectral embedding tensor learning is to capture consistent and complementary information by seeking high-order correlations among multiple views. The strategy of removing missing instances is adopted to construct multiple similarity graphs for incomplete multiple views. Consequently, this strategy provides an intuitive and feasible way to construct multiple similarity graphs. Extensive experimental results on multiview datasets demonstrate the effectiveness of the two spectral embedding fusion methods within the USETL framework.

Accurate and automatic detection of pelvic lymph nodes in computed tomography (CT) scans is critical for diagnosing lymph node metastasis in colorectal cancer, which in turn plays a crucial role in its staging, treatment planning, surgical guidance, and postoperative follow-up of colorectal cancer. However, achieving high detection sensitivity and specificity poses a challenge due to the small and variable sizes of these nodes, as well as the presence of numerous similar signals within the complex pelvic CT image. To tackle these issues, we propose a 3D feature-aware online-tuning network (FAOT-Net) that introduces a novel 1.5-stage structure to seamlessly integrate detection and refinement via our online candidate tuning process and takes advantage of multi-level information through the tailored feature flow. Furthermore, we redesign the anchor fitting and anchor matching strategies to further improve detection performance in a nearly hyperparameter-free manner. Our framework achieves the FROC score of 52.8 and the sensitivity of 91.7% with 16 false positives per scan on the PLNDataset. Code will be available at: github.com/SCUsomebody/FAOT-Net/.

Deep off-policy actor-critic algorithms have been successfully applied to challenging tasks in continuous control. However, these methods typically suffer from the poor sample efficiency problem, limiting their widespread adoption in real-world domains. To mitigate this issue, we propose a novel actor-critic algorithm with weakly pessimistic value estimation and optimistic policy optimization (WPVOP) for continuous control. WPVOP integrates two key ingredients: 1) a weakly pessimistic value estimation, which compensates the pessimism of lower confidence bound in conventional value function (i.e., clipped double Q -learning) to trigger exploration in low-value state-action regions and 2) an optimistic policy optimization algorithm by sampling actions that could benefit the policy learning most toward optimal Q -values for efficient exploration. We theoretically analyze that the proposed weakly pessimistic value estimation method is lower and upper bounded, and empirically show that it could avoid extremely over-optimistic value estimates. We show that these two ideas are largely complementary, and can be fruitfully integrated to improve performance and promote sample efficiency of exploration. We evaluate WPVOP on the suite of continuous control tasks from MuJoCo, achieving state-of-the-art sample efficiency and performance.

Accurately predicting lymph node metastasis (LNM) in colorectal cancer (CRC) contributes to develop appropriate treatment plans and evaluate postoperative prognosis. In clinical practice, preoperative diagnosis of LNM in CRC mostly relies on computed tomography (CT). Nevertheless, CT-based diagnosis of metastatic lymph nodes has a substantial misdiagnosis rate and lacks consistency among clinicians. Additionally, existing deep learning models using CT scans to predict LNM in CRC have not yielded satisfactory results due to insufficient attention given to lymph nodes. To address these issues, we propose an attention-based multiple instance learning (MIL) framework for this challenging task. Our framework incorporates a global-local cross-attention (GLCA) feature fusion module that combines global information from CT slices with local information from lymph nodes. Furthermore, an attention-based pooling approach is utilized to extract critical features and generate a bag representation. Lastly, a nested-neural memory ordinary differential equations (N-nmODE) feature enhancement module significantly augments the expressive capacity of the bag representation. A series of empirical studies show that our method achieves an overall accuracy of 74.7% and AUC of 76.8%, which represents a marked improvement over the physicians specialising in CRC and outperforms existing state-of-the-art methods. Our source code is available at https://github.com/SCU-MI/CAN-MIL.

Accurate pelvis artery segmentation on computerized tomography images is crucial for many diagnoses and surgery related to abdominal diseases, like colorectal cancer metastasis analysis. However, seldom researches considered this, and many minor branch vessels are always broken or even disappear in existing segmentation methods, since they are thin and long, with low contrast, and many variants may exist. To address this problem, we provide a multi-task network with centerline supervision. The centerline extracting task is introduced to offer other domain-specific information and help the network learn the branch vessels. This method mainly includes an early-branch network with an inter-task feature fusion module. In the proposed network, the hard-sharing encoder extracts the shared spatial and global information. The dual-path decoder separates the learning of the tasks and has some fusion blocks to combine and exchange inter-task features. Then, a multi-task learning loss with a consistency correction penalty is designed to keep the training balance through punishing the gap between the two tasks. To evaluate the performance of our method, we conduct ablation and comprehensive experiments on a local pelvis artery dataset. Experimental results in metrics and visualization show that the proposed method achieves superior performance.

Deep neural networks (DNNs) have emerged as a prominent model in medical image segmentation, achieving remarkable advancements in clinical practice. Despite the promising results reported in the literature, the effectiveness of DNNs necessitates substantial quantities of high-quality annotated training data. During experiments, we observe a significant decline in the performance of DNNs on the test set when there exists disruption in the labels of the training dataset, revealing inherent limitations in the robustness of DNNs. In this paper, we find that the neural memory ordinary differential equation (nmODE), a recently proposed model based on ordinary differential equations (ODEs), not only addresses the robustness limitation but also enhances performance when trained by the clean training dataset. However, it is acknowledged that the ODE-based model tends to be less computationally efficient compared to the conventional discrete models due to the multiple function evaluations required by the ODE solver. Recognizing the efficiency limitation of the ODE-based model, we propose a novel approach called the nmODE-based knowledge distillation (nmODE-KD). The proposed method aims to transfer knowledge from the continuous nmODE to a discrete layer, simultaneously enhancing the model's robustness and efficiency. The core concept of nmODE-KD revolves around enforcing the discrete layer to mimic the continuous nmODE by minimizing the KL divergence between them. Experimental results on 18 organs-at-risk segmentation tasks demonstrate that nmODE-KD exhibits improved robustness compared to ODE-based models while also mitigating the efficiency limitation.

Reranking is a critical task used to refine the initial collaborative filtering (CF) recommendation by incorporating information from different viewpoints, such as the extra item side-information and user profile. In this article, a neural reranking-based CF (NRCF) model is proposed to leverage composite viewpoints from the basic CF model and user preference. More precisely, the predictive implicit user preference is first constructed from the initial top-$k$items. The implicit user preference is then aggregated with the explicit user embedding to enrich the user intent representation. Moreover, the traditional listwise loss functions for reranking optimization are suboptimal, due to the fact that they neglect the relative ranking information (ReinRank) between the unobserved and positive items. To address this issue, a novel listwise loss function that leverages relative ranking information, referred to as ReinRank, is proposed for reranking optimization. ReinRank assigns different values to the unobserved items, according to their relative ranking distances between the positive items. Extensive experiments are performed on three public benchmarks and different CF models, in order to demonstrate the effectiveness of NRCF and ReinRank.

Lesion volume segmentation in medical imaging is an effective tool for assessing lesion/tumor sizes and monitoring changes in growth. Since manually segmentation of lesion volume is not only time-consuming but also requires radiological experience, current practices rely on an imprecise surrogate called response evaluation criteria in solid tumors (RECIST). Although RECIST measurement is coarse compared with voxel-level annotation, it can reflect the lesion's location, length, and width, resulting in a possibility of segmenting lesion volume directly via RECIST measurement. In this study, a novel weakly-supervised method called RECISTSup is proposed to automatically segment lesion volume via RECIST measurement. Based on RECIST measurement, a new RECIST measurement propagation algorithm is proposed to generate pseudo masks, which are then used to train the segmentation networks. Due to the spatial prior knowledge provided by RECIST measurement, two new losses are also designed to make full use of it. In addition, the automatically segmented lesion results are used to supervise the model training iteratively for further improving segmentation performance. A series of experiments are carried out on three datasets to evaluate the proposed method, including ablation experiments, comparison of various methods, annotation cost analyses, visualization of results. Experimental results show that the proposed RECISTSup achieves the state-of-the-art result compared with other weakly-supervised methods. The results also demonstrate that RECIST measurement can produce similar performance to voxel-level annotation while significantly saving the annotation cost.

Colorectal cancer (CRC) is a common malignant tumor disease appeared in colon or rectum walls. CRC metastasis often appears with lymph nodes, and the CRC lymph nodes region classification is essential for CRC diagnosis, generally classified as lateral lymph nodes (LLN) and non-lateral lymph nodes (NLLN). Previous CRC diagnosis relied heavily on the physician’s clinical experience, which is a manual and time-consuming process. An automated method based on prior is proposed to CRC lymph node region classification using Convolutional neural networks (CNNs). Two novel priors are proposed, including spatial prior and shape prior. The spatial prior is based on medical domain knowledge to relieve the difficulty of extracting useful features from the complex semantic information of CT images. And the shape prior is proposed through carefully analyzing the dataset, which aims to find an optimal size that can preserve features in the origin CT images and be adaptive to neural network input. Experimental results demonstrate that the proposed method achieves impressive classification performance, in terms of an accuracy of 96.66% and an AUC of 0.9941. Additionally, we apply the proposed method in other medical classification works and it also achieves satisfying results.

False positive reduction(FPR) plays a crucial role in abdominal lymph node detection system, which is of great significance in colorectal cancer diagnosis and early treatment. However, this remains a challenge owing to the complexity of the abdominal tissue. In this study, a simple yet effective method for FPR in the small-size abdominal lymph nodes detection is proposed. For small-size lymph nodes, we design a 3D residual network to adapt to corresponding input, and conveniently adjust the network structure and parameters according to the amount of data. Moreover, we use a multi-context fusion method to integrate the results of multiple models to meet the challenge of vary in lymph node volume. Due to the open-source CTLNDataset with only large lymph node, we utilize a new dataset named PLN-Dataset, which contains a large number of small-size pelvic lymph nodes. The proposed method gets an area under curve(AUC) value of 0.991 in PLNDataset, a good performance metric on the competition performance metric(CPM) with a score of 0.837, and also achieve competitive results in CTLNDataset. The result shows that the proposed method is effective and robust for small-size abdominal lymph nodes.

Computed tomography (CT) scanning is a fast and painless procedure that can capture clear imaging information beneath the abdomen and is widely used to help diagnose and monitor disease progress. The pelvic lymph node is a key indicator of colorectal cancer metastasis. In the traditional process, an experienced radiologist must read all the CT scanning images slice by slice to track the lymph nodes for future diagnosis. However, this process is time-consuming, exhausting, and subjective due to the complex pelvic structure, numerous blood vessels, and small lymph nodes. Therefore, automated methods are desirable to make this process easier. Currently, the available open-source CTLNDataset only contains large lymph nodes. Consequently, a new data set called PLNDataset, which is dedicated to lymph nodes within the pelvis, is constructed to solve this issue. A two-level annotation calibration method is proposed to guarantee the quality and correctness of pelvic lymph node annotation. Moreover, a novel system composed of a keyframe localization network and a lymph node detection network is proposed to detect pelvic lymph nodes in CT scanning images. The proposed method makes full use of two kinds of prior knowledge: spatial prior knowledge for keyframe localization and anchor prior knowledge for lymph node detection. A series of experiments are carried out to evaluate the proposed method, including ablation experiments, comparing other state-of-the-art methods, and visualization of results. The experimental results demonstrate that our proposed method outperforms other methods on PLNDataset and CTLNDataset. This system is expected to be applied in future clinical practice.

Radiotherapy is an indispensable part of adjuvant therapy for cancer that improves local control, overall survival, and the opportunity for good quality of life. Organ delineation and dose plan design are the key steps in the treatment. Organ delineation controls the area of radiotherapy and dose planning controls its intensity. However, both tasks are time-consuming, exhausting, and subjective, and automated methods are desirable. Although automated methods have been studied, the previous studies either focus on organ segmentation or dose prediction, without considering them from a holistic perspective. In this paper, we treat organ segmentation and dose prediction as similar tasks, and propose an error correction framework to improve their performance based on the same mechanism. The proposed error correction framework consists of a prediction network and a calibration network. The biggest difference between our framework and previous studies is that the state-of-the-art networks can be used as a prediction network or calibration network, and then the performance can be improved by the error correction mechanism. To evaluate the framework, we conducted a series of experiments on dose prediction and organ segmentation. These experimental results show that the framework is superior to other state-of-the-art methods in both tasks.

Most popular clustering methods map raw image data into a projection space in which the clustering assignment is obtained with the vanilla k-means approach. In this article, we discovered a novel prior, namely, there exists a common invariance when assigning an image sample to clusters using different metrics. In short, different distance metrics will lead to similar soft clustering assignments on the manifold. Based on such a novel prior, we propose a novel clustering method by minimizing the discrepancy between pairwise sample assignments for each data point. To the best of our knowledge, this could be the first work to reveal the sample-assignment invariance prior based on the idea of treating labels as ideal representations. Furthermore, the proposed method is one of the first end-to-end clustering approaches, which jointly learns clustering assignment and representation. Extensive experimental results show that the proposed method is remarkably superior to 16 state-of-the-art clustering methods on five image data sets in terms of four evaluation metrics.

Graph construction plays an important role in graph-oriented subspace learning. However, most existing approaches cannot simultaneously consider the global and local structures of high-dimensional data. In order to solve this deficiency, we propose a symmetric low-rank preserving projection (SLPP) framework incorporating a symmetric constraint and a local regularization into low-rank representation learning for subspace learning. Under this framework, SLPP-M is incorporated with manifold regularization as its local regularization while SLPP-S uses sparsity regularization. Besides characterizing the global structure of high-dimensional data by a symmetric low-rank representation, both SLPP-M and SLPP-S effectively exploit the local manifold and geometric structure by incorporating manifold and sparsity regularization, respectively. The similarity matrix is successfully learned by solving the nuclear-norm minimization optimization problem . Combined with graph embedding techniques, a transformation matrix effectively preserves the low-dimensional structure features of high-dimensional data. In order to facilitate classification by exploiting available labels of training samples , we also develop a supervised version of SLPP-M and SLPP-S under the SLPP framework, named S-SLPP-M and S-SLPP-S, respectively. Experimental results in face, handwriting and object recognition applications demonstrate the efficiency of the proposed algorithm for subspace learning.

Residual classifiers are common in dictionary-based multiclass classification. This paper proposes the concept of performance functions for residual classifiers. A performance function for multiclass classifications is a conceptual measurement function that combines local and global measurements. In general, the performance function is nonlinear. To explore the properties of the performance function, we employ the Taylor series expansion technique and derive a family of measurement functions. Specifically, the linear measurement and the quadratic measurement (QM) are derived. By exploiting the effect of the higher order terms in the performance function as well as the fundamental nondecreasing constrain, we derive the normalized QM (NQM). We present the classifier for multiclass classification using the proposed measurements. The proposed algorithms are tested against frontal faces and handwritten digit recognition tasks. Our tests show that the QM classifier achieves competitive classification results compared with baseline methods. NQM shows better stability with different parameter configurations.

This brief deals with the problem of minor component analysis (MCA). Artificial neural networks can be exploited to achieve the task of MCA. Recent research works show that convergence of neural networks based MCA algorithms can be guaranteed if the learning rates are less than certain thresholds. However, the computation of these thresholds needs information about the eigenvalues of the autocorrelation matrix of data set, which is unavailable in online extraction of minor component from input data stream. In this correspondence, we introduce an adaptive learning rate into the OJAn MCA algorithm, such that its convergence condition does not depend on any unobtainable information, and can be easily satisfied in practical applications.