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- ArticleSeptember 2024
Identify Disease-Associated MiRNA-miRNA Pairs Through Deep Tensor Factorization and Semi-supervised Learning
Artificial Neural Networks and Machine Learning – ICANN 2024Pages 71–86https://doi.org/10.1007/978-3-031-72353-7_6AbstractMicroRNAs (miRNAs) are a class of small non-coding RNAs that play a significant regulatory role in the development of disease. Researchers have explored a variety of computational methods to predict the association between miRNA and disease, which ...
- ArticleSeptember 2024
Transferability of Non-contrastive Self-supervised Learning to Chronic Wound Image Recognition
Artificial Neural Networks and Machine Learning – ICANN 2024Pages 427–444https://doi.org/10.1007/978-3-031-72353-7_31AbstractChronic wounds pose significant challenges in medical practice, necessitating effective treatment approaches and reduced burden on healthcare staff. Computer-aided diagnosis (CAD) systems offer promising solutions to enhance treatment outcomes. ...
- ArticleSeptember 2024
KnowMIM: a Self-supervised Pre-training Framework Based on Knowledge-Guided Masked Image Modeling for Retinal Vessel Segmentation
Artificial Neural Networks and Machine Learning – ICANN 2024Pages 412–426https://doi.org/10.1007/978-3-031-72353-7_30AbstractMainstream segmentation algorithms currently rely on supervised learning and thus require large pixel-labelled datasets for training. However, manually labelling regions of interest in medical images is both time-consuming and expertise-demanding, ...
- ArticleSeptember 2024
ComplicaCode: Enhancing Disease Complication Detection in Electronic Health Records Through ICD Path Generation
Artificial Neural Networks and Machine Learning – ICANN 2024Pages 29–43https://doi.org/10.1007/978-3-031-72353-7_3AbstractThe target of Electronic Health Record (EHR) coding is to find the diagnostic codes according to the EHRs. In previous research, researchers have preferred to do multi-classification on the EHR coding task; most of them encode the EHR first and ...
- ArticleSeptember 2024
SCST: Spatial Consistent Swin Transformer for Multi-focus Biomedical Microscopic Image Fusion
Artificial Neural Networks and Machine Learning – ICANN 2024Pages 399–411https://doi.org/10.1007/978-3-031-72353-7_29AbstractThis paper studies multi-focus biomedical microscopic image fusion. Recently, deep convolutional neural network (CNN) based approaches have achieved promising performance in multi-focus image fusion, but most of them cannot obtain spatially ...
- ArticleSeptember 2024
SCANet: Dual Attention Network for Alzheimer’s Disease Diagnosis Based on Gated Residual and Spatial Asymmetry Mechanisms
Artificial Neural Networks and Machine Learning – ICANN 2024Pages 384–398https://doi.org/10.1007/978-3-031-72353-7_28AbstractConvolutional neural networks, combined with attention mechanisms, can effectively extract global and local features from structural magnetic resonance images to aid in the diagnosis of Alzheimer’s disease (AD). However, the attention mechanism ...
- ArticleSeptember 2024
Point-Based Weakly Supervised 2.5D Cell Segmentation
Artificial Neural Networks and Machine Learning – ICANN 2024Pages 343–358https://doi.org/10.1007/978-3-031-72353-7_25AbstractVolumetric microscopic images show cells in their natural state and solve various problems inherent to 2D projections. The development of competent Deep Learning methods to segment cells in 3D images is, however, held back by the extremely time-...
- ArticleSeptember 2024
MSD-HAM-Net: A Multi-modality Fusion Network of PET/CT Images for the Prognosis of DLBCL Patients
Artificial Neural Networks and Machine Learning – ICANN 2024Pages 314–327https://doi.org/10.1007/978-3-031-72353-7_23Abstract18F-FDG PET/CT images have been proven promising for the prognosis of Diffuse Large B-cell Lymphoma (DLBCL) patients. However, the implicit drawbacks of images constrain their wide applications. In this paper, we propose a fusion solution which ...
- ArticleSeptember 2024
Hop-Gated Graph Attention Network for ASD Diagnosis via PC-Based Graph Regularization Sparse Representation
Artificial Neural Networks and Machine Learning – ICANN 2024Pages 287–298https://doi.org/10.1007/978-3-031-72353-7_21AbstractAutism spectrum Disorder (ASD) is a neurodevelopmental disorder that severely affects the daily life of patients. Deep learning is widely used in the diagnosis of ASD. However, it is difficult to construct brain functional network (BFN) due to the ...
- ArticleSeptember 2024
CurSegNet: 3D Dental Model Segmentation Network Based on Curve Feature Aggregation
Artificial Neural Networks and Machine Learning – ICANN 2024Pages 245–259https://doi.org/10.1007/978-3-031-72353-7_18AbstractThe precise segmentation of 3D dental models obtained from intraoral scanners (IOS) is a primary task in computer-aided orthodontic diagnosis and treatment. Existing dental model segmentation networks mainly focus on extracting local aggregation ...
- ArticleSeptember 2024
Classification of Dehiscence Defects in Titanium and Zirconium Dental Implants
Artificial Neural Networks and Machine Learning – ICANN 2024Pages 230–244https://doi.org/10.1007/978-3-031-72353-7_17AbstractIn oral health, the accurate diagnosis of conditions like periapical lesions and dehiscences, especially those associated with titanium and zirconia implants, presents significant challenges due to the complex nature of such dental pathologies, ...
- ArticleSeptember 2024
CellSpot: Deep Learning-Based Efficient Cell Center Detection in Microscopic Images
Artificial Neural Networks and Machine Learning – ICANN 2024Pages 215–229https://doi.org/10.1007/978-3-031-72353-7_16AbstractCells play a fundamental role in sustaining life by performing numerous functions crucial for the survival of living organisms. The detection of cells holds paramount importance in the validation and analysis of biological hypotheses, as it offers ...
- ArticleSeptember 2024
Blood Cell Detection and Self-Attention-Based Mixed Attention Mechanism
Artificial Neural Networks and Machine Learning – ICANN 2024Pages 203–214https://doi.org/10.1007/978-3-031-72353-7_15AbstractDeep learning-based microscopy image analysis has made significant progress. However, accurately identifying cell targets among dense and complex distributions is still challenging. This study introduces a single-stage anchor-free Blood Cell ...
- ArticleSeptember 2024
Advancing Free-Breathing Cardiac Cine MRI: Retrospective Respiratory Motion Correction Via Kspace-and-Image Guided Diffusion Model
- Hongming Guo,
- Ziqing Huang,
- Qian Yuan,
- Hanbo Song,
- Zhiyan Liu,
- Xianzhao Feng,
- Anqi Liu,
- Min Liu,
- Ke Li,
- Ruixi Zhou
Artificial Neural Networks and Machine Learning – ICANN 2024Pages 189–202https://doi.org/10.1007/978-3-031-72353-7_14AbstractThis study introduces the Double-Guidance Diffusion Model (DB-DDPM), a novel conditional Denoising Diffusion Probabilistic Model (DDPM) designed specifically for high-quality correction of respiratory motion, a prevalent challenge in cardiac cine ...
- ArticleSeptember 2024
Adaptive Fusion Boundary-Enhanced Multilayer Perceptual Network (FBAIM-Net) for Enhanced Polyp Segmentation in Medical Imaging
Artificial Neural Networks and Machine Learning – ICANN 2024Pages 179–188https://doi.org/10.1007/978-3-031-72353-7_13AbstractAccurate polyp segmentation in medical image analysis is vital for early diagnosis and treatment planning, particularly in scenarios with diverse polyp shapes and sizes. This study introduces the Adaptive Fusion Boundary-Enhanced Multilayer ...
- ArticleSeptember 2024
ProTeM: Unifying Protein Function Prediction via Text Matching
- Ming Qin,
- Xun Li,
- Yuhao Wang,
- Zhenping Li,
- Hongbin Ye,
- Zongbing Wang,
- Weihao Gao,
- Shangsong Liang,
- Qiang Zhang,
- Keyan Ding
Artificial Neural Networks and Machine Learning – ICANN 2024Pages 132–146https://doi.org/10.1007/978-3-031-72353-7_10AbstractThe exponential availability of protein sequences has led to the dominance of the pretraining-then-finetuning paradigm for protein function prediction. However, finetuning a pretrained protein language model for diverse downstream tasks requires ...
- ArticleSeptember 2024
A Deep Learning Multi-omics Framework to Combine Microbiome and Metabolome Profiles for Disease Classification
Artificial Neural Networks and Machine Learning – ICANN 2024Pages 3–14https://doi.org/10.1007/978-3-031-72353-7_1AbstractMicrobiome and metabolome contain information about host disease. Therefore, a multi-omics analysis of these data types can provide key constraints for disease classification. However, due to multi-omics data’s complex and high-dimensional nature, ...
