Dynamic hierarchical multi-scale fusion network with axial MLP for medical image segmentation

Cheng, Zhikun; Wang, Liejun

doi:10.1038/s41598-023-32813-z

Cited by 6 publications

(5 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Radiomics models were constructed on the training set using various classifiers, including LR, SVM, KNN, RF, ET, XGBoost, LightGBM, and MLP [ 26 – 33 ]. To evaluate the accuracy of the radiomics features in predicting PC lesions, the generated radiomics models were evaluated using the testing set, and the diagnostic performance of the models was quantitatively compared using AUC.…”

Section: Discussionmentioning

confidence: 99%

Noninvasive prediction of lymph node metastasis in pancreatic cancer using an ultrasound-based clinicoradiomics machine learning model

Wen,

Chen,

Tang

et al. 2024

BioMed Eng OnLine

View full text Add to dashboard Cite

Objectives This study was designed to explore and validate the value of different machine learning models based on ultrasound image-omics features in the preoperative diagnosis of lymph node metastasis in pancreatic cancer (PC). Methods This research involved 189 individuals diagnosed with PC confirmed by surgical pathology (training cohort: n = 151; test cohort: n = 38), including 50 cases of lymph node metastasis. Image-omics features were extracted from ultrasound images. After dimensionality reduction and screening, eight machine learning algorithms, including logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), extra trees (ET), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and multilayer perceptron (MLP), were used to establish image-omics models to predict lymph node metastasis in PC. The best omics prediction model was selected through ROC curve analysis. Machine learning models were used to analyze clinical features and determine variables to establish a clinical model. A combined model was constructed by combining ultrasound image-omics and clinical features. Decision curve analysis (DCA) and a nomogram were used to evaluate the clinical application value of the model. Results A total of 1561 image-omics features were extracted from ultrasound images. 15 valuable image-omics features were determined by regularization, dimension reduction, and algorithm selection. In the image-omics model, the LR model showed higher prediction efficiency and robustness, with an area under the ROC curve (AUC) of 0.773 in the training set and an AUC of 0.850 in the test set. The clinical model constructed by the boundary of lesions in ultrasound images and the clinical feature CA199 (AUC = 0.875). The combined model had the best prediction performance, with an AUC of 0.872 in the training set and 0.918 in the test set. The combined model showed better clinical benefit according to DCA, and the nomogram score provided clinical prediction solutions. Conclusion The combined model established with clinical features has good diagnostic ability and can be used to predict lymph node metastasis in patients with PC. It is expected to provide an effective noninvasive method for clinical decision-making, thereby improving the diagnosis and treatment of PC.

show abstract

Section: Discussionmentioning

confidence: 99%

Noninvasive prediction of lymph node metastasis in pancreatic cancer using an ultrasound-based clinicoradiomics machine learning model

Wen,

Chen,

Tang

et al. 2024

BioMed Eng OnLine

View full text Add to dashboard Cite

show abstract

“…Building on this, Guo, Tang, Han, Chen, Wu, Xu, Xu, and Wang [43] proposed AS-MLP, which emphasized local feature interactions through axial shifts, marking a significant shift towards localizing MLP applications. This trend towards localized feature processing is further evident in the work of Cheng and Wang [44], who introduced a dynamic hierarchical multi-scale fusion network in medical image segmentation, emphasizing detailed lesion identification. Similarly, Schmidt-Mengin et al [45] and An et al [46] explored axial MLP architectures, focusing on specific medical conditions like multiple sclerosis and enhancing foreground segmentation in medical images, respectively.…”

Section: Rethinking Hierarchical and Axial Feature Integration In Mlpsmentioning

confidence: 94%

Deciphering the Efficacy of No-Attention Architectures in Computed Tomography Image Classification: A Paradigm Shift

Alzahrani

2024

Mathematics

View full text Add to dashboard Cite

The burgeoning domain of medical imaging has witnessed a paradigm shift with the integration of AI, particularly deep learning, enhancing diagnostic precision and expediting the analysis of Computed Tomography (CT) images. This study introduces an innovative Multilayer Perceptron-driven model, DiagnosticMLP, which sidesteps the computational intensity of attention-based mechanisms, favoring a no-attention architecture that leverages Fourier Transforms for global information capture and spatial gating units for local feature emphasis. This study’s methodology encompasses a sophisticated augmentation and patching strategy at the input level, followed by a series of MLP blocks designed to extract hierarchical features and spatial relationships, culminating in a global average pooling layer before classification. Evaluated against state-of-the-art MLP-based models including MLP-Mixer, FNet, gMLP, and ResMLP across diverse and extensive CT datasets, including abdominal, and chest scans, DiagnosticMLP demonstrated a remarkable ability to converge efficiently, with competitive accuracy, F1 scores, and AUC metrics. Notably, in datasets featuring kidney and abdomen disorders, the model showcased superior generalization capabilities, underpinned by its unique design that addresses the complexity inherent in CT imaging. The findings in terms of accuracy and precision-recall balance posit DiagnosticMLP as an exceptional outperforming alternative to attention-reliant models, paving the way for streamlined, efficient, and scalable AI tools in medical diagnostics, reinforcing the potential for AI-augmented precision medicine without the dependency on attention-based architectures.

show abstract

“…DSC expresses between 0 and 1, with a higher value representing a better segmentation result. (Cheng and Wang 2023)…”

Section: Implementation Details and Evaluation Metricsmentioning

confidence: 99%

“…In other words, it shows the number of true positive results divided by the number of all samples that should have been identified as positive. (Cheng and Wang 2023)…”

Section: Implementation Details and Evaluation Metricsmentioning

confidence: 99%

See 1 more Smart Citation

Cardiac MRI segmentation using shifted-window multilayer perceptron mixer networks

Abouei,

Pan,

et al. 2024

Phys. Med. Biol.

View full text Add to dashboard Cite

Purpose: In this work, we proposed a deep-learning segmentation algorithm for cardiac magnetic resonance imaging (MRI) to aid in contouring of the left ventricle (LV), right ventricle (RV), and Myocardium (Myo). Methods: We proposed a shifted window multilayer perceptron (Swin-MLP) mixer network which is built upon a 3D U-shaped symmetric encoder-decoder structure. We evaluated our proposed network using public data from 100 individuals. The network performance was quantitatively evaluated using 3D volume similarity between the ground truth contours and the predictions using Dice score coefficient, sensitivity, and precision as well as 2D surface similarity using Hausdorff distance (HD), mean surface distance (MSD) and residual mean square distance (RMSD). We benchmarked the performance against two other current leading edge networks known as Dynamic UNet and Swin-UNetr on the same public dataset. Results: The proposed network achieved the following volume similarity metrics when averaged over three cardiac segments: Dice = 0.952±0.017, precision = 0.948±0.016, sensitivity = 0.956±0.022. The average surface similarities were HD = 1.521±0.121 mm, MSD = 0.266±0.075 mm, and RMSD = 0.668±0.288 mm. The network shows statistically significant improvement in comparison to the Dynamic UNet and Swin-UNetr algorithms for most volumetric and surface metrics with p-value less than 0.05. Overall, the proposed Swin-MLP mixer network demonstrates better or comparable performance than competing methods. Conclusions: The proposed Swin-MLP mixer network demonstrates more accurate segmentation performance compared to current leading edge methods. This robust method demonstrates the potential to streamline clinical workflows for multiple applications.

show abstract

Dynamic hierarchical multi-scale fusion network with axial MLP for medical image segmentation

Cited by 6 publications

References 47 publications

Noninvasive prediction of lymph node metastasis in pancreatic cancer using an ultrasound-based clinicoradiomics machine learning model

Noninvasive prediction of lymph node metastasis in pancreatic cancer using an ultrasound-based clinicoradiomics machine learning model

Deciphering the Efficacy of No-Attention Architectures in Computed Tomography Image Classification: A Paradigm Shift

Cardiac MRI segmentation using shifted-window multilayer perceptron mixer networks

Contact Info

Product

Resources

About