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Multi-dataset Collaborative Learning for Liver Tumor Segmentation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Automatic segmentation of biomedical images has emerged due to its potential in improving real-world clinical processes and has achieved great success in recent years thanks to the development of deep learning. However, it is the limited availability...

Asymmetric Adaptive Heterogeneous Network for Multi-Modality Medical Image Segmentation.

IEEE transactions on medical imaging
Existing studies of multi-modality medical image segmentation tend to aggregate all modalities without discrimination and employ multiple symmetric encoders or decoders for feature extraction and fusion. They often overlook the different contribution...

Unsupervised Domain Adaptation for Cross-Modality Cerebrovascular Segmentation.

IEEE journal of biomedical and health informatics
Cerebrovascular segmentation from time-of-flight magnetic resonance angiography (TOF-MRA) and computed tomography angiography (CTA) is essential in providing supportive information for diagnosing and treatment planning of multiple intracranial vascul...

Multi-Scale Dynamic Sparse Token Multi-Instance Learning for Pathology Image Classification.

IEEE journal of biomedical and health informatics
In many challenging breast cancer pathology images, the proportion of truly informative tumor regions is extremely limited. The disparity between the essential information required for clinical diagnosis (Tumor area less than 10$\%$) and the vast amo...

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation.

IEEE transactions on medical imaging
Anatomical abnormality detection and report generation of chest X-ray (CXR) are two essential tasks in clinical practice. The former aims at localizing and characterizing cardiopulmonary radiological findings in CXRs, while the latter summarizes the ...

HisynSeg: Weakly-Supervised Histopathological Image Segmentation via Image-Mixing Synthesis and Consistency Regularization.

IEEE transactions on medical imaging
Tissue semantic segmentation is one of the key tasks in computational pathology. To avoid the expensive and laborious acquisition of pixel-level annotations, a wide range of studies attempt to adopt the class activation map (CAM), a weakly-supervised...

MHFNet: A Multimodal Hybrid-Embedding Fusion Network for Automatic Sleep Staging.

IEEE journal of biomedical and health informatics
Scoring sleep stages is essential for evaluating the status of sleep continuity and comprehending its structure. Despite previous attempts, automating sleep scoring remains challenging. First, most existing works did not fuse local and global tempora...

FlexibleSleepNet:A Model for Automatic Sleep Stage Classification Based on Multi-Channel Polysomnography.

IEEE journal of biomedical and health informatics
In the task of automatic sleep stage classification, deep learning models often face the challenge of balancing temporal-spatial feature extraction with computational complexity. To address this issue, this study introduces FlexibleSleepNet, a lightw...

Progressive Distillation With Optimal Transport for Federated Incomplete Multi-Modal Learning of Brain Tumor Segmentation.

IEEE journal of biomedical and health informatics
Multi-modal Magnetic Resonance Imaging (MRI) provide sufficient complementary information for brain tumor segmentation, however, most current approaches rely on complete modalities and may collapse with incomplete modalities. Moreover, most existing ...

SWMA-UNet: Multi-Path Attention Network for Improved Medical Image Segmentation.

IEEE journal of biomedical and health informatics
In recent years, deep learning achieves significant advancements in medical image segmentation. Research finds that integrating Transformers and CNNs effectively addresses the limitations of CNNs in managing long-distance dependencies and understandi...