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MulitDeepsurv: survival analysis of gastric cancer based on deep learning multimodal fusion models

BIOMEDICAL OPTICS EXPRESS 2025年第1期16卷 126-141页

作者： Mao, Songren Liu, Jie Taiyuan Normal Univ Coll Comp Sci & Technol Jinzhong 030619 Peoples R China Taiyuan Inst Technol Comp Engn Dept Taiyuan 030008 Peoples R China

Gastric cancer is a leading cause of cancer-related deaths globally. As mortality rates continue to rise, predicting cancer survival using multimodal data-including histopathological images, genomic data, and clinical information-has become increasingly crucial. However, extracting effective predictive features from this complex data has posed challenges for survival analysis due to the high dimensionality and heterogeneity of histopathology images and genomic data. Furthermore, existing methods often lack sufficient interaction between intra- and inter- modal features, significantly impacting model performance. To address these challenges, we developed a deep learning-based multimodal feature fusion model, MultiDeepsurv, designed to predict the survival of gastric cancer patients by integrating histopathological images, clinical data, and gene expression data. Our approach includes a two-branch hybrid network, GLFUnet, which leverages the attention mechanism for enhanced pathology image representation learning. Additionally, we employ a graph convolutional neural network (GCN) to extract features from gene expression data and clinical information. To capture the correlations between different modalities, we utilize the SFusion fusion strategy that employs a self-attention mechanism to learn potential correlations across modalities. Finally, these deeply processed features are fed into Cox regression models for an end-to-end survival analysis. Comprehensive experiments and analyses conducted on a gastric cancer cohort from The Cancer Genome Atlas (TCGA) demonstrate that our proposed MultiDeepsurv model outperforms other methods in terms of prognostic accuracy, with a C-index of 0.806 and an AUC of 0.842. (c) 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： Deep learning image enhancement Machine learning neural networks Spatial filtering stochastic gradient descent

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SeisGAN: Improving Seismic image Resolution and Reducing Random Noise Using a Generative Adversarial Network

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MATHEMATICAL GEOSCIENCES 2024年第4期56卷 723-749页

作者： Lin, Lei Zhong, Zhi Cai, Chuyang Li, Chenglong Zhang, Heng China Univ Geosci Key Lab Theory & Technol Petr Explorat & Dev Hubei Wuhan Peoples R China Monash Univ Melbourne Australia

Seismic images are essential for understanding the subsurface geological structure and resource distribution. However, the accuracy and certainty of geological analysis using seismic images are limited by the resolution and signal-to-noise ratio. Simultaneously improving resolution and suppressing random noise with traditional methods can be quite challenging. This research proposes a new approach called SeisGAN which leverages a generative adversarial network to address the challenge at hand. Due to the lack of high-resolution noiseless and low-resolution noisy seismic data, stochastic parameter control is employed to simulate a vast range of diverse, paired seismic data for SeisGAN training. The results on the synthetic dataset demonstrate that the proposed method is effective in enhancing the resolution and suppressing the random noise in the original images. Spectrum analysis shows that the proposed method increases the bandwidth of the original data, primarily at high frequencies. Ablation experiments reveal that, under similar conditions, SeisGAN outperforms traditional convolutional neural networks. Incorporating the VGG loss in the generator loss function improves the model's ability to recover high-frequency details. The application of the technique on two publicly available field seismic datasets indicates SeisGAN's excellent generalizability, despite being trained only on synthetic seismic data. Compared with bicubic interpolation and traditional noise suppression and resolution enhancement methods, SeisGAN is capable of effectively suppressing the random noise and enhancing the dominant frequency of field seismic data, making it easier to identify adjacent thin layers and fault features, even for small-scale faults. The zoomed images are clearer and easier to interpret. Furthermore, an example of automatic machine fault identification demonstrates the significant contribution of the SeisGAN-enhanced image to accurate fault recognition.

关键词： Seismic image processing Seismic resolution Seismic signal-to-noise ratio Deep learning

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Arithmetic Optimization for Coinciding Diabetic Retinopathy and Diabetic Macular Edema Grading based on Self-Attention Convolutional neural Network

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BIOMEDICAL signal processing AND CONTROL 2024年第PartB期95卷

作者： Devi, T. M. Karthikeyan, P. Syed Ammal Engn Coll Dept Comp Sci & Engn Madurai Rameswaram Rd Landai Tamil Nadu India Thiagarajar Coll Engn Dept Informat Technol V3JJ VJ3 Thiruparankundram Tamil Nadu India

This paper proposes a Self-Attention Convolutional neural Network (SACNN) optimized with Arithmetic Optimization Algorithm (AOA) for coinciding Diabetic Retinopathy (DR) and Diabetic Macular Edema Grading (DMEG) (SACNN-AOA-DR-DMEG). Initially, the input image is collected from 2 openly available benchmark datasets, namely Messidor and ISBI 2018 IDRiD. Then the input image is pre-processing using Altered Phase Preserving Dynamic Range Compression (APPDRC) for reducing noise from the imageries. SACNN receives the pre-processed imageries. The SACNN has three modules: (i) plane attention module, (ii) depth attention module, (iii) Attention Fusion Module. DR and DME features are extracted by plane attention module and depth attention module of SACNN. Attention Fusion Module receives extracted characteristics for categorizing and grading DR and DME disorders. SACNN does not adopt any optimization techniques to guarantee accurate DR and DME grading disorders. That's why, Arithmetic Optimization Algorithm (AOA) is deemed to optimize the SACNN weight parameters. The proposed technique is implemented in Python. The proposed SACNN-AOA-DR-DMEG method provides 11.18%, 18.99% and 23.76% higher accuracy for diabetic retinopathy grading;11.52%, 29.62% and 20.38% higher accuracy for DMEG;33.39%, 22%, 39.26% lower computation time on Messidor data compared with the existing methods, such as AMGNN-DR-DMEG, LCNN-DR-DMEG, and FFN-DR-DMEG respectively.

关键词： Arithmetic optimization algorithm Messidor dataset Self -attention convolutional neural network

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Chromosome segmentation and classification: an updated review

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KNOWLEDGE AND INFORMATION SYSTEMS 2025年第2期67卷 977-1011页

作者： Somasundaram, Devaraj Madian, Nirmala Goh, Kam Meng Suresh, S. Vellore Inst Technol Sch Elect Engn SENSE Dept Micro & Nanoelect Vellore India Dr NGP Inst Technol Dept Biomed Engn Coimbatore India Tunku Abdul Rahman Univ Management & Technol Fac Engn & Technol Ctr Multimodal Signal Proc Kuala Lumpur Malaysia Med Syst Chennai India

Karyotyping is a study of chromosomes to identify various chromosomal aberrations related to structure and number. Chromosome image analysis involves challenging issues related to overlapping and touching of chromosomes. Chromosome segmentation and classification generally focus on separating overlapping and touching chromosomes. The analysis methods start from conventional image processing methods to advanced machine learning techniques. These methods are broadly classified into low-level and high-level methods. The low-level methods are thresholding-based approaches, edge detection, feature extraction techniques like active contours and watershed approaches and machine learning for classification. The high-level methods are deep learning algorithms like convolutional neural networks (CNNs), U-Net, autoencoder architectures. These methods help in improving accuracy and automate the process of chromosome segmentation and classification. High-level approaches can handle complexity in chromosome overlaps which provides better segmentation results. The approach learns complicated patterns and structures of chromosome images, which helps in achieving better classification accuracy. The challenges are: (i) working on large and annotated dataset for training deep learning models and (ii) suffer issues with new dataset even in they perform better during training phase. The solution for all these can be a hybrid approach that combines conventional method with modern approaches. This survey gives readers a basic understanding of automated karyotyping and future direction in this domain.

关键词： Chromosome Classification Karyotyping Metaphase spread Segmentation

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Optimized Parameter-Efficient Deep Learning Systems via Reversible Jump Simulated Annealing

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2024年第6期18卷 1010-1023页

作者： Marsh, Peter Kuruoglu, Ercan Engin Univ Town Shenzhen Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China

We utilize the non-convex optimization method simulated annealing enriched with reversible jumps to enable a model selection capacity for deep learning models in a model size aware context. By using simulated annealing enriched with reversible jumps, we can yield a robust stochastic learning of the hidden posterior distribution of the structure, simultaneously constructing a more focused and certain estimate of the structure, all while making use of all the data. Being based upon Markov-chain learning methods, we constructed our priors to favor smaller and simpler architectures, allowing us to converge on the set of globally optimal models that are additionally parameter-efficient, seeking low parameter count deep models that retain good predictive accuracy. We demonstrate the capability on standard image recognition with CIFAR-10, as well as performing model selection on time-series tasks, realizing networks with competitive performance as compared to competing non-convex optimization methods such as genetic algorithms, random search, and Gaussian process based Bayesian optimization, while being less than half the size.

关键词： Simulated annealing Optimization neural networks Long short term memory image recognition Task analysis Data models Deep learning systems model selection reversible jump Markov chain Monte Carlo simulated annealing

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DEEP neural NETWORK MODELS TRAINED WITH A FIXED RANDOM CLASSIFIER TRANSFER BETTER ACROSS DOMAINS 49

DEEP NEURAL NETWORK MODELS TRAINED WITH A FIXED RANDOM CLASS...

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49th IEEE International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Ali, Hafiz Tiomoko Michieli, Umberto Moon, Ji Joong Kim, Daehyun Ozay, Mete Samsung Res UK London England Samsung Res Korea Seoul South Korea

ISBN: (纸本)9798350344868;9798350344851

The recently discovered neural collapse (NC) phenomenon states that the last-layer weights of Deep neural Networks (DNN), converge to the so-called Equiangular Tight Frame (ETF) simplex, at the terminal phase of their training. This ETF geometry is equivalent to vanishing within-class variability of the last layer activations. Inspired by NC properties, we explore in this paper the transferability of DNN models trained with their last layer weight fixed according to ETF. This enforces class separation by eliminating class covariance information, effectively providing implicit regularization. We show that DNN models trained with such a fixed classifier significantly improve transfer performance, particularly on out-of-domain datasets. On a broad range of fine-grained image classification datasets, our approach outperforms i) baseline methods that do not perform any covariance regularization (up to 22%), as well as ii) methods that explicitly whiten covariance of activations throughout training (up to 19%). Our findings suggest that DNNs trained with fixed ETF classifiers offer a powerful mechanism for improving transfer learning across domains.

关键词： Transfer learning neural Collapse Random Projections Deep Learning Models

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A Review of image Enhancement methods Based on stochastic Resonance of Neuronal Networks 5

A Review of Image Enhancement Methods Based on Stochastic Re...

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5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Sun, Ximin Niu, Pingjuan Li, Yong Li, Xiaoming Zhao, Bingzhen Zhang, Bin Liu, Dan Sun, Bo State Gride Ecommerce Technology CO. LTD Tiangong University Laboratory of Metaverse in Electric Power Industry Tian'jin China Tiangong University Laboratory of Metaverse in Electric Power Industry Tian'jin China State Gride Ecommerce Technology CO. LTD Laboratory of Metaverse in Electric Power Industry Tian'jin China

ISBN: (纸本)9798350355925

stochastic resonance has always been a research hotspot in the field of image processing. The ability of the nervous system to detect signals is closely related to nonlinear and collective behavior in neural mediators. Neuronal noise is the most significant factor influencing this ability, and it is a natural endogenous fluctuation in brain activity. Experimental and theoretical studies have shown that noise is beneficial, not harmful, for the normal functioning of the nervous system. In this regard, it is generally agreed that noise of appropriate intensity can participate in the rhythmic activity of the brain, and that noise-induced oscillations enhance the performance of weak signal processing, especially when the frequency of the signal is approximately noise-induced rhythmic oscillations. This behavior in the biological nervous system can be explained by the concept of 'stochastic resonance'. Correspondingly, the stochastic resonance mechanism of neurons is used to denoise the image, which has a good effect. In this paper, we first introduce an image processing method using random resonance of neurons. Then, the commonly used evaluation indicators in the field of image processing are sorted out. Finally, the development trend of image processing using random resonance of neurons is prospected. © 2024 IEEE.

关键词： Circuit resonance

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Riemannian Generalized Gaussian Distributions on the Space of SPD Matrices for image Classification

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IEEE ACCESS 2024年 12卷 26096-26109页

作者： Abbad, Zakariae El Maliani, Ahmed Drissi El Hassouni, Mohammed Abbassi, Mohamed Tahar Kadaoui Bombrun, Lionel Berthoumieu, Yannick Mohammed V Univ Rabat ENSIAS Rabat 10000 Morocco Mohammed V Univ Rabat Fac Sci LRIT Rabat IT Ctr Rabat 10000 Morocco Mohammed V Univ Rabat FLSH Rabat 10000 Morocco Sidi Mohamed Ben Abdellah Univ Fac Sci Dhar El Mahraz Lab Math Sci & Applicat Fes 30000 Morocco Univ Bordeaux CNRS Bordeaux INP IMSUMR 5218 F-33400 Talence France Bordeaux Sci Agro F-33175 Gradignan France

The space of symmetric positive definite (SPD) matrices, denoted as $P_{m}$ , plays a crucial role in various domains, including computer vision, medical imaging, and signal processing. Its significance lies in its capacity to represent the underlying structure in nonlinear data using its Riemannian geometry. Nevertheless, a notable gap exists in the absence of statistical distributions capable of characterizing the statistical properties of data within this space. This paper proposes a new Riemannian Generalized Gaussian distribution (RGGD) on that space. The major contributions of this paper are, first of all, providing the exact expression of the probability density function (PDF) of the RGGD model, as well as an exact expression of the normalizing factor. Furthermore, an estimation of parameters is given using the maximum likelihood of this distribution. The second contribution involves exploiting the second-order statistics of feature maps derived from the first layers of deep convolutional neural networks (DCNNs) through the RGGD stochastic model in an image classification framework. Experiments were carried out on four well-known datasets, and the results demonstrate the efficiency and competitiveness of the proposed model.

关键词： Symmetric positive definite matrices generalized Gaussian distribution texture Riemannian geometry Rao's distance Riemannian metric

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DEEP SUB-image SAMPLING BASED DEFENSE AGAINST SPATIAL-DOMAIN ADVERSARIAL STEGANOGRAPHY 49

DEEP SUB-IMAGE SAMPLING BASED DEFENSE AGAINST SPATIAL-DOMAIN...

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49th IEEE International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Huang, Xinyu Cao, Yuwen Ohtsuki, Tomoaki Northwestern Polytech Univ Sch Cybersecur Xian Peoples R China Donghua Univ Coll Informat Sci & Technol Shanghai Peoples R China Keio Univ Dept Informat & Comp Sci Tokyo Japan

ISBN: (纸本)9798350374520;9798350374513

Deep steganalyzer combined with neural networks has achieved great success in image classification over recent years. However, it suffers from the following persistent challenges: i) Deep steganalyzer is extremely vulnerable and has the risk of being attacked via adversarial steganography when performing the image classification tasks;ii) Pre-processing based methods aiming to remove adversarial perturbations from cover images jeopardize the accuracy performance, as the involved steganographic signal will be wiped off as well. In this context, to defend against adversarial attacks by an adversary, we propose an adversarial steganography detection scheme based on the pre-processing and feature migration. In brief, sub-images are sampled to obtain the dimensionality of the extracted features, which are usually used to expand them while reducing the effect brought by adversarial perturbations. In particular, by computing statistical features together with normalizing the features, our approach can improve the classification accuracy of the samples. Our experimental results show that the proposed approach is capable of detecting adversarial steganographic image with an accuracy gain of up to 35.9% over the state-of-the-art methods.

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R2D2 image RECONSTRUCTION WITH MODEL UNCERTAINTY QUANTIFICATION IN RADIO ASTRONOMY 32

R2D2 IMAGE RECONSTRUCTION WITH MODEL UNCERTAINTY QUANTIFICAT...

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32nd European signal processing Conference (EUSIPCO)

作者： Aghabiglou, Amir Chu, Chung San Dabbech, Arwa Wiaux, Yves Heriot Watt Univ Inst Sensors Signals & Syst Edinburgh EH14 4AS Midlothian Scotland

ISBN: (纸本)9789464593617;9798331519773

The "Residual-to-Residual DNN series for high-Dynamic range imaging" (R2D2) approach was recently introduced for Radio-Interferometric (RI) imaging in astronomy. R2D2's reconstruction is formed as a series of residual images, iteratively estimated as outputs of Deep neural Networks (DNNs) taking the previous iteration's image estimate and associated data residual as inputs. In this work, we investigate the robustness of the R2D2 image estimation process, by studying the uncertainty associated with its series of learned models. Adopting an ensemble averaging approach, multiple series can be trained, arising from different random DNN initializations of the training process at each iteration. The resulting multiple R2D2 instances can also be leveraged to generate "R2D2 samples", from which empirical mean and standard deviation endow the algorithm with a joint estimation and uncertainty quantification functionality. Focusing on RI imaging, and adopting a telescope-specific approach, multiple R2D2 instances were trained to encompass the most general observation setting of the Very Large Array (VLA). Simulations and real-data experiments confirm that: (i) R2D2's image estimation capability is superior to that of the state-of-the-art algorithms;(ii) its ultra-fast reconstruction capability (arising from series with only few DNNs) makes the computation of multiple reconstruction samples and of uncertainty maps practical even at large image dimension;(iii) it is characterized by a very low model uncertainty.

关键词： Computational methods Deep learning Astronomy image processing Aperture synthesis

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