Search Results for author:
Found 36 papers, 11 papers with code
BrainMVP: Multi-modal Vision Pre-training for Brain Image Analysis using Multi-parametric MRI
Then, a novel pre-training paradigm is proposed for the multi-modal MRI data, addressing the issue of missing modalities and achieving multi-modal information fusion.
Multi-modal Data Binding for Survival Analysis Modeling with Incomplete Data and Annotations
Survival analysis stands as a pivotal process in cancer treatment research, crucial for predicting patient survival rates accurately.
Cost-effective Instruction Learning for Pathology Vision and Language Analysis
Here we propose a cost-effective instruction learning framework for conversational pathology named as CLOVER.
Pathology-knowledge Enhanced Multi-instance Prompt Learning for Few-shot Whole Slide Image Classification
Prompt learning based on the pre-trained models (\eg, CLIP) appears to be a promising scheme for this setting; however, current research in this area is limited, and existing algorithms often focus solely on patch-level prompts or confine themselves to language prompts.
OpenMEDLab: An Open-source Platform for Multi-modality Foundation Models in Medicine
The emerging trend of advancing generalist artificial intelligence, such as GPTv4 and Gemini, has reshaped the landscape of research (academia and industry) in machine learning and many other research areas.
Learning A Multi-Task Transformer Via Unified And Customized Instruction Tuning For Chest Radiograph Interpretation
We first compose a multi-task training dataset comprising 13. 4 million instruction and ground-truth pairs (with approximately one million radiographs) for the customized tuning, involving both image- and pixel-level tasks.
Boosting Dermatoscopic Lesion Segmentation via Diffusion Models with Visual and Textual Prompts
Image synthesis approaches, e. g., generative adversarial networks, have been popular as a form of data augmentation in medical image analysis tasks.
Text-guided Foundation Model Adaptation for Pathological Image Classification
In this paper, we propose to Connect Image and Text Embeddings (CITE) to enhance pathological image classification.
Exploring Data Redundancy in Real-world Image Classification through Data Selection
Deep learning models often require large amounts of data for training, leading to increased costs.
MedFMC: A Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image Classification
Foundation models, often pre-trained with large-scale data, have achieved paramount success in jump-starting various vision and language applications.
T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imaging
Lesion segmentation in medical imaging has been an important topic in clinical research.
Improving Pneumonia Localization via Cross-Attention on Medical Images and Reports
Both the classification and localization are trained in conjunction and once trained, the model can be utilized for both the localization and characterization of pneumonia using only the input image.
Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures
Building robust deep learning-based models requires diverse training data, ideally from several sources.
Self-supervised Image-text Pre-training With Mixed Data In Chest X-rays
In this work, we introduce an image-text pre-training framework that can learn from these raw data with mixed data inputs, i. e., paired image-text data, a mixture of paired and unpaired data.
Transformer Query-Target Knowledge Discovery (TEND): Drug Discovery from CORD-19
To stimulate COVID-19 research, we release an influenza clinical trials and antiviral analogies dataset used in conjunction with the COVID-19 Open Research Dataset Challenge (CORD-19) literature dataset in the study.
Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan
To facilitate CT analysis, recent efforts have focused on computer-aided characterization and diagnosis, which has shown promising results.
Going to Extremes: Weakly Supervised Medical Image Segmentation
Here, we suggest using minimal user interaction in the form of extreme point clicks to train a segmentation model which, in effect, can be used to speed up medical image annotation.
Learning Image Labels On-the-fly for Training Robust Classification Models
We apply the attention-on-label scheme on the classification task of a synthetic noisy CIFAR-10 dataset to prove the concept, and then demonstrate superior results (3-5% increase on average in multiple disease classification AUCs) on the chest x-ray images from a hospital-scale dataset (MIMIC-CXR) and hand-labeled dataset (OpenI) in comparison to regular training paradigms.
Weakly supervised one-stage vision and language disease detection using large scale pneumonia and pneumothorax studies
The architectural modifications address three obstacles -- implementing a supervised vision and language detection method in a weakly supervised fashion, incorporating clinical referring expression natural language information, and generating high fidelity detections with map probabilities.
Multi-Domain Image Completion for Random Missing Input Data
Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e. g., brain tumor segmentation from multi-parametric magnetic resonance imaging (MRI).
Enhancing Foreground Boundaries for Medical Image Segmentation
Object segmentation plays an important role in the modern medical image analysis, which benefits clinical study, disease diagnosis, and surgery planning.
When Radiology Report Generation Meets Knowledge Graph
In addition, we proposed a new evaluation metric for radiology image reporting with the assistance of the same composed graph.
Training Models 20X Faster in Medical Image Analysis
Analyzing high-dimensional medical images (2D/3D/4D CT, MRI, histopathological images, etc.)
Correlation via Synthesis: End-to-end Image Generation and Radiogenomic Learning Based on Generative Adversarial Network
In this work, we investigate the potential of an end-to-end method fusing gene code with image features to generate synthetic pathology image and learn radiogenomic map simultaneously.
Weakly supervised segmentation from extreme points
Here, we propose to use minimal user interaction in the form of extreme point clicks in order to train a segmentation model that can, in turn, be used to speed up the annotation of medical images.
Correlation via synthesis: end-to-end nodule image generation and radiogenomic map learning based on generative adversarial network
Radiogenomic map linking image features and gene expression profiles is useful for noninvasively identifying molecular properties of a particular type of disease.
When Unseen Domain Generalization is Unnecessary? Rethinking Data Augmentation
We rethink data augmentation for medical 3D images and propose a deep stacked transformations (DST) approach for domain generalization.
Spatio-Temporal Convolutional LSTMs for Tumor Growth Prediction by Learning 4D Longitudinal Patient Data
Results validate that the ST-ConvLSTM produces a Dice score of 83. 2%+-5. 1% and a RVD of 11. 2%+-10. 8%, both significantly outperforming (p<0. 05) other compared methods of linear model, ConvLSTM, and generative adversarial network (GAN) under the metric of predicting future tumor volumes.
Attention-Guided Curriculum Learning for Weakly Supervised Classification and Localization of Thoracic Diseases on Chest Radiographs
In addition, highly confident samples (measured by classification probabilities) and their corresponding class-conditional heatmaps (generated by the CNN) are extracted and further fed into the AGCL framework to guide the learning of more distinctive convolutional features in the next iteration.
TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-rays
Chest X-rays are one of the most common radiological examinations in daily clinical routines.
NegBio: a high-performance tool for negation and uncertainty detection in radiology reports
Negative and uncertain medical findings are frequent in radiology reports, but discriminating them from positive findings remains challenging for information extraction.
Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database
Then, a triplet network is utilized to learn lesion embeddings with a sequential sampling strategy to depict their hierarchical similarity structure.
DeepLesion: Automated Deep Mining, Categorization and Detection of Significant Radiology Image Findings using Large-Scale Clinical Lesion Annotations
We categorize the collection of lesions using an unsupervised deep mining scheme to generate clustered pseudo lesion labels.
ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosis of many lung diseases.
Unsupervised Joint Mining of Deep Features and Image Labels for Large-scale Radiology Image Categorization and Scene Recognition
The recent rapid and tremendous success of deep convolutional neural networks (CNN) on many challenging computer vision tasks largely derives from the accessibility of the well-annotated ImageNet and PASCAL VOC datasets.
Unsupervised Category Discovery via Looped Deep Pseudo-Task Optimization Using a Large Scale Radiology Image Database
Obtaining semantic labels on a large scale radiology image database (215, 786 key images from 61, 845 unique patients) is a prerequisite yet bottleneck to train highly effective deep convolutional neural network (CNN) models for image recognition.
