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Found 19 papers, 13 papers with code
Finding Local Diffusion Schrödinger Bridge using Kolmogorov-Arnold Network
The experiment shows that our LDSB significantly improves the quality and efficiency of image generation using the same pre-trained denoising network and the KAN for optimising is only less than 0. 1MB.
Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge
This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis.
MedFILIP: Medical Fine-grained Language-Image Pre-training
2) A knowledge injector is proposed to construct relationships between categories and visual attributes, which help the model to make judgments based on image features, and fosters knowledge extrapolation to unfamiliar disease categories.
Efficient MedSAMs: Segment Anything in Medical Images on Laptop
Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice.
Improving Representation of High-frequency Components for Medical Visual Foundation Models
Foundation models have recently attracted significant attention for their impressive generalizability across diverse downstream tasks.
Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differences
Employing HiPaS, we have conducted an anatomical study of pulmonary vasculature on 11, 784 participants in China (six sites), discovering a new association of pulmonary vessel anatomy with sex, age, and disease states: vessel abundance suggests a significantly higher association with females than males with slightly decreasing with age, and is also influenced by certain diseases, under the controlling of lung volumes.
Unsupervised Decomposition Networks for Bias Field Correction in MR Image
Many retrospective algorithms were developed to facilitate the bias correction, to which the deep learning-based methods outperformed.
Efficient automatic segmentation for multi-level pulmonary arteries: The PARSE challenge
Efficient automatic segmentation of multi-level (i. e. main and branch) pulmonary arteries (PA) in CTPA images plays a significant role in clinical applications.
The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge
It is intended to resolve the above-mentioned problems and promote the development of both intracranial hemorrhage segmentation and anisotropic data processing.
Position-prior Clustering-based Self-attention Module for Knee Cartilage Segmentation
The morphological changes in knee cartilage (especially femoral and tibial cartilages) are closely related to the progression of knee osteoarthritis, which is expressed by magnetic resonance (MR) images and assessed on the cartilage segmentation results.
ULTRA: Uncertainty-aware Label Distribution Learning for Breast Tumor Cellularity Assessment
Therefore, automatic TC estimation is significant in clinical practice.
KIEGLFN: A unified acne grading framework on face images
Methods: A unified acne grading framework that can be generalized to apply referring to different grading criteria is developed.
Ranked #3 on
Acne Severity Grading
on ACNE04
(Accuracy metric)
Transformer Network for Significant Stenosis Detection in CCTA of Coronary Arteries
However, the complexity of coronary artery plaques that cause CAD makes the automatic detection of coronary artery stenosis in Coronary CT angiography (CCTA) a difficult task.
The SARAS Endoscopic Surgeon Action Detection (ESAD) dataset: Challenges and methods
For an autonomous robotic system, monitoring surgeon actions and assisting the main surgeon during a procedure can be very challenging.
Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge
In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the segmentation of LGE CMR.
An Automatic Cardiac Segmentation Framework based on Multi-sequence MR Image
A CNN segmentation model was trained based on the augmented training data by leave-one-out strategy.
Multi-step Cascaded Networks for Brain Tumor Segmentation
Automatic brain tumor segmentation method plays an extremely important role in the whole process of brain tumor diagnosis and treatment.
VoxelAtlasGAN: 3D Left Ventricle Segmentation on Echocardiography with Atlas Guided Generation and Voxel-to-voxel Discrimination
And cGAN advantageously fuses substantial 3D spatial context information from 3D echocardiography by self-learning structured loss; 2) For the first time, it embeds the atlas into an end-to-end optimization framework, which uses 3D LV atlas as a powerful prior knowledge to improve the inference speed, address the lower contrast and the limited annotation problems of 3D echocardiography; 3) It combines traditional discrimination loss and the new proposed consistent constraint, which further improves the generalization of the proposed framework.
Multi-views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Images
Methods: In this paper, we propose a direct volumes prediction method based on the end-to-end deep convolutional neural networks (CNN).
