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Found 17 papers, 4 papers with code
Effective Lymph Nodes Detection in CT Scans Using Location Debiased Query Selection and Contrastive Query Representation in Transformer
Lymph node (LN) assessment is a critical, indispensable yet very challenging task in the routine clinical workflow of radiology and oncology.
ReSynthDetect: A Fundus Anomaly Detection Network with Reconstruction and Synthetic Features
Detecting anomalies in fundus images through unsupervised methods is a challenging task due to the similarity between normal and abnormal tissues, as well as their indistinct boundaries.
Learning Anatomically Consistent Embedding for Chest Radiography
Self-supervised learning (SSL) approaches have recently shown substantial success in learning visual representations from unannotated images.
Source-Free Domain Adaptation for Medical Image Segmentation via Prototype-Anchored Feature Alignment and Contrastive Learning
To tackle the source data-absent problem, we present a novel two-stage source-free domain adaptation (SFDA) framework for medical image segmentation, where only a well-trained source segmentation model and unlabeled target data are available during domain adaptation.
Region and Spatial Aware Anomaly Detection for Fundus Images
ReSAD transfers a pre-trained model to extract the features of normal fundus images and applies the Region-and-Spatial-Aware feature Combination module (ReSC) for pixel-level features to build a memory bank.
DualStreamFoveaNet: A Dual Stream Fusion Architecture with Anatomical Awareness for Robust Fovea Localization
Accurate fovea localization is essential for analyzing retinal diseases to prevent irreversible vision loss.
Coarse Retinal Lesion Annotations Refinement via Prototypical Learning
Deep-learning-based approaches for retinal lesion segmentation often require an abundant amount of precise pixel-wise annotated data.
Weakly Supervised Online Action Detection for Infant General Movements
Although general movements assessment(GMA) has shown promising results in early CP detection, it is laborious.
MDT-Net: Multi-domain Transfer by Perceptual Supervision for Unpaired Images in OCT Scan
Furthermore, we take the transfer results as additional training data for fluid segmentation to prove the advantage of our model indirectly, i. e., in the task of data adaptation and augmentation.
Bilateral-ViT for Robust Fovea Localization
The fovea is an important anatomical landmark of the retina.
A Location-Sensitive Local Prototype Network for Few-Shot Medical Image Segmentation
Despite the tremendous success of deep neural networks in medical image segmentation, they typically require a large amount of costly, expert-level annotated data.
Learning Differential Diagnosis of Skin Conditions with Co-occurrence Supervision using Graph Convolutional Networks
Skin conditions are reported the 4th leading cause of nonfatal disease burden worldwide.
Extreme Consistency: Overcoming Annotation Scarcity and Domain Shifts
In this paper, we introduce \emph{extreme consistency}, which overcomes the above limitations, by maximally leveraging unlabeled data from the same or a different domain in a teacher-student semi-supervised paradigm.
ErrorNet: Learning error representations from limited data to improve vascular segmentation
In this paper, we propose a segmentation framework called ErrorNet, which learns to correct these segmentation mistakes through the repeated process of injecting systematic segmentation errors to the segmentation result based on a learned shape prior, followed by attempting to predict the injected error.
Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation
The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks.
Automatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network
We evaluated our model using 84 chest CT scans from the LIDC and 154 pathological cases from the LTRC datasets.
Surrogate Supervision for Medical Image Analysis: Effective Deep Learning From Limited Quantities of Labeled Data
We investigate the effectiveness of a simple solution to the common problem of deep learning in medical image analysis with limited quantities of labeled training data.
