논문 검색
-
Multimodal sparse fusion transformer network with spatio-temporal decoupling for breast tumor classification.
TL;DRA novel Multimodal Sparse Fusion Transformer Network (MSFT-Net), which achieves superior performance in multimodal breast tumor classification compared to state-of-the-art methods, providing fast and reliable support for…
FULLTEXT: Hypergraph Convolution -
Raman spectral unmixing of breast cancer tissues via continuous wavelet transform and TransUnet.
Raman spectroscopy has been proved to have the potential to accurately diagnose a variety of diseases, and novel Raman probes or instruments for clinical applications have been constantly developed. However, biological tissues are usually s…
FULLTEXT: Hypergraph Convolution -
Explainable Lightweight Model Using Low-Rank and Convolutional Block Attention for Pancreatic Cancer Diagnosis.
[BACKGROUND] Early and accurate pancreatic cancer (PC) detection remains a major clinical challenge. [METHODS] We introduce a novel hybrid deep learning framework for automated classification of CT images, which requires fewer computationa…
FULLTEXT: Hypergraph Convolution -
Clinician-deployable deep hypergraph model integrating clinical and CT radiomics predicts immunotherapy outcomes in NSCLC.
Current image-based deep learning models that predict the benefits of immunotherapy in non-small cell lung cancer (NSCLC) require high-performance hardware. We aimed to develop and externally validate a clinician-operable prognostic model t…
FULLTEXT: Hypergraph Convolution -
X-LAT-Net: An Interpretable Lightweight Axial Transformer Network for Pancreatic CT Segmentation.
Accurate segmentation of the pancreas in CT images is notoriously difficult due to its deep-seated anatomical position, complex morphological variations, and low contrast against surrounding soft tissues. Although deep learning has signific…
FULLTEXT: Hypergraph Convolution -
WTAM-YOLO: a YOLOv11-based method for pulmonary nodule detection.
Lung cancer is among the malignancies with the highest incidence and mortality rates worldwide, and it poses a serious threat to human health. Increasing the accuracy of pulmonary nodule detection in CT images is essential for the early dia…
FULLTEXT: Hypergraph Convolution -
Breast pathology image segmentation based on DESB-net: a fusion strategy of detail enhancement, edge focus, and cross-layer connections.
TL;DRImprovements in the DESB-Net model significantly enhance the model’s ability to capture small lesions and edge details, improve segmentation accuracy and robustness, and maintain high computational efficiency, highlighti…
FULLTEXT: Hypergraph Convolution -
A deep learning framework for breast cancer diagnosis using Swin Transformer and Dual-Attention Multi-scale Fusion Network.
Breast cancer is among the most prevalent cancers affecting women worldwide, and early detection through mammography is critical to reducing mortality rates. Convolutional neural networks (CNNs) have demonstrated notable effectiveness in cl…
FULLTEXT: Hypergraph Convolution -
HyperSynergyX: Synergistic Drug Combination Prediction via Hypergraph Modeling and Knowledge Graph-Enhanced Retrieval-Augmented Generation.
Drug combination therapy is pivotal for complex diseases, but identifying synergistic three-drug regimens remains challenging due to both combinatorial explosion and the opacity of existing computational models. To address this, we introduc…
FULLTEXT: Hypergraph Convolution -
A manta ray-bayesian optimization approach for hyperparameter-tuned convolutional neural networks in lung cancer classification.
Lung cancer remains a global health challenge that is unavoidable. Despite the advances in lung cancer classification using deep learning models, the performance remains highly dependent on hyperparameter selection, whereas conventional gri…
FULLTEXT: Hypergraph Convolution -
Polyp image segmentation based on parallel dilated convolution and dual attention mechanisms.
Colorectal cancer is usually caused by malignant transformation of early colon polyps. Early polyps are benign, but if left untreated, they can progress to cancer. Early diagnosis of polyps can significantly improve the survival rate of pat…
FULLTEXT: Hypergraph Convolution -
Mining whole-brain information with deep learning to predict EGFR mutation and subtypes in brain-metastatic NSCLC: A multicenter study.
[BACKGROUND] Epidermal growth factor receptor (EGFR) and its mutation subtypes play a pivotal role in the treatment of non-small cell lung cancer (NSCLC) patients. Therefore, developing an accurate, noninvasive quantitative method to predic…
FULLTEXT: Hypergraph Convolution -
Mamba‑MFNet: A hierarchical supervised network based on the fusion of axial and cross-modal attention for breast DCE‑MRI tumor segmentation.
Early detection and accurate segmentation of breast cancer are critical for improving cure rates and optimizing treatment strategies. However, existing 3D segmentation methods face significant limitations in global context modeling and effi…
FULLTEXT: Hypergraph Convolution -
Pancreatic SBRT on four modern platforms: dosimetric and radiobiological gains with the Ethos ring-gantry linac versus TrueBeam, Halcyon, and helical tomotherapy.
[PURPOSE] To perform the first comprehensive dosimetric and radiobiological comparison of four modern delivery platforms for stereotactic body radiation therapy (SBRT) in pancreatic cancer: a conventional C-arm linac (TrueBeam), two establi…
FULLTEXT: Hypergraph Convolution -
[CGP-Net: Cross-modal guided prior network for precise gastric cancer segmentation].
Precise segmentation of gastric cancer computed tomography (CT) images is a critical step for clinical precision diagnosis and treatment. However, it currently faces two major challenges: the low contrast between tumors and surrounding norm…
FULLTEXT: Hypergraph Convolution -
MultiScale hierarchical attention network for stain free breast cancer detection in microscopic hyperspectral imaging.
Microscopic hyperspectral imaging (MHSI) of unstained tissue provides quantitative, label-free cues for pathology, but practical diagnosis is hindered by weak morphological contrast and high-dimensional spectra. Patch-wise classification is…
FULLTEXT: Hypergraph Convolution -
Enhanced breast cancer detection framework based on YOLOv11n with multi-scale feature calibration.
Breast cancer poses a persistent global health challenge, making early diagnosis indispensable for reducing mortality and improving patient prognosis. However, conventional detection paradigms are frequently impeded by the inherent complexi…
FULLTEXT: Hypergraph Convolution -
Spatial-Spectral Deep Learning for Prostate Cancer Tissue Classification in Infrared Spectroscopy.
Modern methods of infrared (IR) spectroscopy yield full IR absorbance spectra in arrays, forming hyperspectral images. End-to-end processing of these images via deep learning seems ideal for exploiting their high dimensionality and wealth o…
FULLTEXT: Hypergraph Convolution -
Multimodal Graph Learning With Multi-Hypergraph Reasoning Networks for Focal Liver Lesion Classification in Multimodal Magnetic Resonance Imaging.
Multimodal magnetic resonance imaging (MRI) is instrumental in differentiating liver lesions. The major challenge involves modeling reliable connections and simultaneously learning complementary information across various MRI sequences. Whi…
FULLTEXT: Hypergraph Convolution -
Efficient Video Polyp Segmentation by Deformable Alignment and Local Attention.
Accurate and efficient Video Polyp Segmentation (VPS) is vital for the early detection of colorectal cancer and the effectivetreatment of polyps. However, achieving this remains highly challenging due to the inherent difficulty in modeling …
FULLTEXT: Hypergraph Convolution