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Found 10 papers, 8 papers with code
Devil is in Channels: Contrastive Single Domain Generalization for Medical Image Segmentation
In C$^2$SDG, the shallower features of each image and its style-augmented counterpart are extracted and used for contrastive training, resulting in the disentangled style representations and structure representations.
Transformer-based Annotation Bias-aware Medical Image Segmentation
This paper proposes a Transformer-based Annotation Bias-aware (TAB) medical image segmentation model, which tackles the annotator-related bias via modeling annotator preference and stochastic errors.
Instance-specific Label Distribution Regularization for Learning with Label Noise
Specifically, we estimate the noisy posterior under the supervision of noisy labels, and approximate the batch-level noise transition matrix by estimating the inter-class correlation matrix with neither anchor points nor pseudo anchor points.
ProSFDA: Prompt Learning based Source-free Domain Adaptation for Medical Image Segmentation
In this paper, we propose a \textbf{Pro}mpt learning based \textbf{SFDA} (\textbf{ProSFDA}) method for medical image segmentation, which aims to improve the quality of domain adaption by minimizing explicitly the domain discrepancy.
Boundary-Aware Network for Kidney Parsing
Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate segmentation of kidney structures on computed tomography angiography (CTA) images remains challenging, due to the variable sizes of kidney tumors and the ambiguous boundaries between kidney structures and their surroundings.
Boundary-Aware Network for Abdominal Multi-Organ Segmentation
In this paper, we propose a boundary-aware network (BA-Net) to segment abdominal organs on CT scans and MRI scans.
Label Propagation for 3D Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis
Carotid vessel wall segmentation is a crucial yet challenging task in the computer-aided diagnosis of atherosclerosis.
Modeling Annotator Preference and Stochastic Annotation Error for Medical Image Segmentation
Manual annotation of medical images is highly subjective, leading to inevitable and huge annotation biases.
Domain and Content Adaptive Convolution based Multi-Source Domain Generalization for Medical Image Segmentation
In the DAC module, a dynamic convolutional head is conditioned on the predicted domain code of the input to make our model adapt to the unseen target domain.
Learning from Ambiguous Labels for Lung Nodule Malignancy Prediction
According to the consistency and reliability of their annotations, we divide nodules into three sets: a consistent and reliable set (CR-Set), an inconsistent set (IC-Set), and a low reliable set (LR-Set).
