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Found 15 papers, 7 papers with code
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.
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.
EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer
The accuracy, sensitivity, and specificity of tumor classification is 88. 6%, 94. 1%, and 85. 3%, respectively.
Breast Anatomy Enriched Tumor Saliency Estimation
Then we refine the layers by integrating a non-semantic breast anatomy model to solve the problems of incomplete mammary layers.
Tumor Saliency Estimation for Breast Ultrasound Images via Breast Anatomy Modeling
First, we model breast anatomy and decompose breast ultrasound image into layers using Neutro-Connectedness; then utilize the layers to generate the foreground and background maps; and finally propose a novel objective function to estimate the tumor saliency by integrating the foreground map, background map, adaptive center bias, and region-based correlation cues.
A Hybrid Framework for Tumor Saliency Estimation
Automatic tumor segmentation of breast ultrasound (BUS) image is quite challenging due to the complicated anatomic structure of breast and poor image quality.
BUSIS: A Benchmark for Breast Ultrasound Image Segmentation
Breast ultrasound (BUS) image segmentation is challenging and critical for BUS Comput-er-Aided Diagnosis (CAD) systems.
Automatic Breast Ultrasound Image Segmentation: A Survey
Breast cancer is one of the leading causes of cancer death among women worldwide.
Neutro-Connectedness Cut
Interactive image segmentation is a challenging task and receives increasing attention recently; however, two major drawbacks exist in interactive segmentation approaches.
An algorithm for Left Atrial Thrombi detection using Transesophageal Echocardiography
And the dynamic features are formed by using the information of its neighbor frames in the sequence.
