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Found 20 papers, 11 papers with code
Vision-Language Synthetic Data Enhances Echocardiography Downstream Tasks
High-quality, large-scale data is essential for robust deep learning models in medical applications, particularly ultrasound image analysis.
Enhancing Efficiency in Vision Transformer Networks: Design Techniques and Insights
Intrigued by the inherent ability of the human visual system to identify salient regions in complex scenes, attention mechanisms have been seamlessly integrated into various Computer Vision (CV) tasks.
Do High-Performance Image-to-Image Translation Networks Enable the Discovery of Radiomic Features? Application to MRI Synthesis from Ultrasound in Prostate Cancer
Finally, a detailed qualitative assessment by five medical doctors indicated a lack of low level feature discovery in image to image translation tasks.
MEDDAP: Medical Dataset Enhancement via Diversified Augmentation Pipeline
Moreover, ensuring that the model remains robust across various scenarios of image capture is crucial in medical domains, especially when dealing with ultrasound images that vary based on the settings of different devices and the manual operation of the transducer.
Multi-Scale Feature Fusion using Parallel-Attention Block for COVID-19 Chest X-ray Diagnosis
Under the global COVID-19 crisis, accurate diagnosis of COVID-19 from Chest X-ray (CXR) images is critical.
Ambiguous Medical Image Segmentation using Diffusion Models
Collective insights from a group of experts have always proven to outperform an individual's best diagnostic for clinical tasks.
Augmenting endometriosis analysis from ultrasound data with deep learning
Our objective in this work is to investigate the potential of deep learning methods to classify endometriosis from ultrasound data.
Diffusion Models for Medical Image Analysis: A Comprehensive Survey
Then, we provide a systematic taxonomy of diffusion models in the medical domain and propose a multi-perspective categorization based on their application, imaging modality, organ of interest, and algorithms.
Multi-Feature Vision Transformer via Self-Supervised Representation Learning for Improvement of COVID-19 Diagnosis
To improve the diagnostic performance of CXR imaging a growing number of studies have investigated whether supervised deep learning methods can provide additional support.
Orientation-guided Graph Convolutional Network for Bone Surface Segmentation
Due to imaging artifacts and low signal-to-noise ratio in ultrasound images, automatic bone surface segmentation networks often produce fragmented predictions that can hinder the success of ultrasound-guided computer-assisted surgical procedures.
Simultaneous Bone and Shadow Segmentation Network using Task Correspondence Consistency
Segmenting both bone surface and the corresponding acoustic shadow are fundamental tasks in ultrasound (US) guided orthopedic procedures.
Realistic Ultrasound Image Synthesis for Improved Classification of Liver Disease
With the success of deep learning-based methods applied in medical image analysis, convolutional neural networks (CNNs) have been investigated for classifying liver disease from ultrasound (US) data.
Multi-Feature Semi-Supervised Learning for COVID-19 Diagnosis from Chest X-ray Images
The requirement for a large amount of labeled data is one of the major problems of deep learning methods when deployed in the medical domain.
Medical Transformer: Gated Axial-Attention for Medical Image Segmentation
The proposed Medical Transformer (MedT) is evaluated on three different medical image segmentation datasets and it is shown that it achieves better performance than the convolutional and other related transformer-based architectures.
Ranked #1 on
Medical Image Segmentation
on Brain US
Chest X-ray Image Phase Features for Improved Diagnosis of COVID-19 Using Convolutional Neural Network
The enhanced images, together with the original CXR data, are used as an input to our proposed CNN architecture.
KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation
To overcome this issue, we propose using an overcomplete convolutional architecture where we project our input image into a higher dimension such that we constrain the receptive field from increasing in the deep layers of the network.
Ranked #1 on
Medical Image Segmentation
on RITE
KiU-Net: Towards Accurate Segmentation of Biomedical Images using Over-complete Representations
Due to its excellent performance, U-Net is the most widely used backbone architecture for biomedical image segmentation in the recent years.
Learning to Segment Brain Anatomy from 2D Ultrasound with Less Data
We show that our method can synthesize high-quality US images for every manipulated segmentation label with qualitative and quantitative improvements over the recent state-of-the-art synthesis methods.
Simultaneous Segmentation and Classification of Bone Surfaces from Ultrasound Using a Multi-feature Guided CNN
Various imaging artifacts, low signal-to-noise ratio, and bone surfaces appearing several millimeters in thickness have hindered the success of ultrasound (US) guided computer assisted orthopedic surgery procedures.
Adversarial Domain Adaptation for Classification of Prostate Histopathology Whole-Slide Images
Automatic and accurate Gleason grading of histopathology tissue slides is crucial for prostate cancer diagnosis, treatment, and prognosis.
