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Found 9 papers, 7 papers with code
APPLE: Adversarial Privacy-aware Perturbations on Latent Embedding for Unfairness Mitigation
Ensuring fairness in deep-learning-based segmentors is crucial for health equity.
Inspecting Model Fairness in Ultrasound Segmentation Tasks
With the rapid expansion of machine learning and deep learning (DL), researchers are increasingly employing learning-based algorithms to alleviate diagnostic challenges across diverse medical tasks and applications.
SRSNetwork: Siamese Reconstruction-Segmentation Networks based on Dynamic-Parameter Convolution
In this paper, we present a high-performance deep neural network for weak target image segmentation, including medical image segmentation and infrared image segmentation.
MobileUtr: Revisiting the relationship between light-weight CNN and Transformer for efficient medical image segmentation
This work revisits the relationship between CNNs and Transformers in lightweight universal networks for medical image segmentation, aiming to integrate the advantages of both worlds at the infrastructure design level.
Slide-SAM: Medical SAM Meets Sliding Window
To address these problems, we propose Slide-SAM, which treats a stack of three adjacent slices as a prediction window.
CMUNeXt: An Efficient Medical Image Segmentation Network based on Large Kernel and Skip Fusion
In order to extract global context information while taking advantage of the inductive bias, we propose CMUNeXt, an efficient fully convolutional lightweight medical image segmentation network, which enables fast and accurate auxiliary diagnosis in real scene scenarios.
Thinking Twice: Clinical-Inspired Thyroid Ultrasound Lesion Detection Based on Feature Feedback
Accurate detection of thyroid lesions is a critical aspect of computer-aided diagnosis.
Multi-Level Global Context Cross Consistency Model for Semi-Supervised Ultrasound Image Segmentation with Diffusion Model
Our approach enables the effective transfer of probability distribution knowledge to the segmentation network, resulting in improved segmentation accuracy.
CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network
However, due to the inherent local characteristics of ordinary convolution operations, U-Net encoder cannot effectively extract global context information.
