Search Results for author:
Found 19 papers, 2 papers with code
Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound
(3) Implementation of a progressive curriculum learning strategy to enable agents to interact with the environment in a progressively challenging manner, thereby enhancing learning efficiency.
EM-Net: Efficient Channel and Frequency Learning with Mamba for 3D Medical Image Segmentation
Convolutional neural networks have primarily led 3D medical image segmentation but may be limited by small receptive fields.
Ctrl-GenAug: Controllable Generative Augmentation for Medical Sequence Classification
In the medical field, the limited availability of large-scale datasets and labor-intensive annotation processes hinder the performance of deep models.
TriAug: Out-of-Distribution Detection for Imbalanced Breast Lesion in Ultrasound
Different diseases, such as histological subtypes of breast lesions, have severely varying incidence rates.
One-Stop Automated Diagnostic System for Carpal Tunnel Syndrome in Ultrasound Images Using Deep Learning
In the reader study, it demonstrated comparable performance with the average performance of the experienced in classifying the CTS, while outperformed that of the inexperienced radiologists in terms of classification metrics (e. g., accuracy score of 3. 59% higher and F1 score of 5. 85% higher).
Is Two-shot All You Need? A Label-efficient Approach for Video Segmentation in Breast Ultrasound
Breast lesion segmentation from breast ultrasound (BUS) videos could assist in early diagnosis and treatment.
PE-MED: Prompt Enhancement for Interactive Medical Image Segmentation
It can prevent the highly unfavorable scenarios, such as encountering a blank mask as the initial input after the first interaction.
Hierarchical Agent-based Reinforcement Learning Framework for Automated Quality Assessment of Fetal Ultrasound Video
Ultrasound is the primary modality to examine fetal growth during pregnancy, while the image quality could be affected by various factors.
Masked Video Modeling with Correlation-aware Contrastive Learning for Breast Cancer Diagnosis in Ultrasound
Breast cancer is one of the leading causes of cancer deaths in women.
Multi-Attribute Attention Network for Interpretable Diagnosis of Thyroid Nodules in Ultrasound Images
To overcome this, we propose a novel deep learning framework called multi-attribute attention network (MAA-Net) that is designed to mimic the clinical diagnosis process.
Fine-grained Correlation Loss for Regression
In this work, we propose to revisit the classic regression tasks with novel investigations on directly optimizing the fine-grained correlation losses.
Personalized Diagnostic Tool for Thyroid Cancer Classification using Multi-view Ultrasound
Furthermore, finding an optimal way to integrate multi-view information also relies on the experience of clinicians and adds further difficulty to accurate diagnosis.
HASA: Hybrid Architecture Search with Aggregation Strategy for Echinococcosis Classification and Ovary Segmentation in Ultrasound Images
The hybrid framework consists of a pre-trained backbone and several searched cells (i. e., network building blocks), which takes advantage of the strengths of both NAS and the expert knowledge from existing convolutional neural networks.
Statistical Dependency Guided Contrastive Learning for Multiple Labeling in Prenatal Ultrasound
In this study, we build a novel multi-label learning (MLL) scheme to identify multiple standard planes and corresponding anatomical structures of fetus simultaneously.
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound
First, our proposed method is general and it can accurately localize multiple SPs in different challenging US datasets.
Agent with Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D Ultrasound
2D US has to perform scanning for each SP, which is time-consuming and operator-dependent.
Contrastive Rendering for Ultrasound Image Segmentation
However, the lack of sharp boundaries in US images still remains an inherent challenge for segmentation.
Region Proposal Network with Graph Prior and IoU-Balance Loss for Landmark Detection in 3D Ultrasound
The performance of the proposed framework is evaluated on a 3D US dataset to detect five key fetal facial landmarks.
Automated fetal brain extraction from clinical Ultrasound volumes using 3D Convolutional Neural Networks
To improve the performance of most neuroimiage analysis pipelines, brain extraction is used as a fundamental first step in the image processing.
