Search Results for author:
Found 15 papers, 9 papers with code
Generative Medical Segmentation
Concretely, GMS employs a robust pre-trained Variational Autoencoder (VAE) to derive latent representations of both images and masks, followed by a mapping model that learns the transition from image to mask in the latent space.
ARHNet: Adaptive Region Harmonization for Lesion-aware Augmentation to Improve Segmentation Performance
In this paper, we propose a foreground harmonization framework (ARHNet) to tackle intensity disparities and make synthetic images look more realistic.
Learning Better Contrastive View from Radiologist's Gaze
Then, we propose a novel augmentation method, i. e., FocusContrast, to learn from radiologists' gaze in diagnosis and generate contrastive views for medical images with guidance from radiologists' visual attention.
Domain Generalization for Mammographic Image Analysis with Contrastive Learning
The training of an efficacious deep learning model requires large data with diverse styles and qualities.
ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models
Large language models (LLMs) have recently demonstrated their potential in clinical applications, providing valuable medical knowledge and advice.
Image Synthesis with Disentangled Attributes for Chest X-Ray Nodule Augmentation and Detection
Moreover, we propose to synthesize nodule CXR images by controlling the disentangled nodule attributes for data augmentation, in order to better compensate for the nodules that are easily missed in the detection task.
Follow My Eye: Using Gaze to Supervise Computer-Aided Diagnosis
In this paper, we demonstrate that the eye movement of radiologists reading medical images can be a new form of supervision to train the DNN-based computer-aided diagnosis (CAD) system.
Knee Cartilage Defect Assessment by Graph Representation and Surface Convolution
Knee osteoarthritis (OA) is the most common osteoarthritis and a leading cause of disability.
Learning Hierarchical Attention for Weakly-supervised Chest X-Ray Abnormality Localization and Diagnosis
However, doing this accurately will require a large amount of disease localization annotations by clinical experts, a task that is prohibitively expensive to accomplish for most applications.
Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning
Specifically, the backbone network is firstly trained with a multi-style and multi-view unsupervised self-learning scheme for the embedding of invariant features to various vendor-styles.
mr2NST: Multi-Resolution and Multi-Reference Neural Style Transfer for Mammography
However, the image styles of different vendors are very distinctive, and there may exist domain gap among different vendors that could potentially compromise the universal applicability of one deep learning model.
A Self-ensembling Framework for Semi-supervised Knee Cartilage Defects Assessment with Dual-Consistency
With dual-consistency checking of the attention in the lesion classification and localization, the two networks can gradually optimize the attention distribution and improve the performance of each other, whereas the training relies on partially labeled data only and follows the semi-supervised manner.
Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumonia
To this end, we develop a dual-sampling attention network to automatically diagnose COVID- 19 from the community acquired pneumonia (CAP) in chest computed tomography (CT).
Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond
The results show that our framework can smoothly synthesize pedestrians on background images of variations and different levels of details.
Ranked #2 on
Scene Text Recognition
on MSDA
ZipNet-GAN: Inferring Fine-grained Mobile Traffic Patterns via a Generative Adversarial Neural Network
Large-scale mobile traffic analytics is becoming essential to digital infrastructure provisioning, public transportation, events planning, and other domains.
