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Found 22 papers, 10 papers with code
MCL: Multi-view Enhanced Contrastive Learning for Chest X-ray Report Generation
Radiology reports are crucial for planning treatment strategies and enhancing doctor-patient communication, yet manually writing these reports is burdensome for radiologists.
Pulmonary Embolism Mortality Prediction Using Multimodal Learning Based on Computed Tomography Angiography and Clinical Data
Imaging features and/or clinical variables were then incorporated into DL models to predict survival outcomes.
MindSemantix: Deciphering Brain Visual Experiences with a Brain-Language Model
MindSemantix generates high-quality captions that are deeply rooted in the visual and semantic information derived from brain activity.
Multi-modality Regional Alignment Network for Covid X-Ray Survival Prediction and Report Generation
In response to the worldwide COVID-19 pandemic, advanced automated technologies have emerged as valuable tools to aid healthcare professionals in managing an increased workload by improving radiology report generation and prognostic analysis.
Structural Entities Extraction and Patient Indications Incorporation for Chest X-ray Report Generation
This process allows the text decoder to attend to discriminative features of X-ray images, assimilate historical diagnostic information from similar cases, and understand the examination intention of patients.
Ranked #1 on
Medical Report Generation
on MIMIC-CXR
(Example-F1-14 metric)
Factual Serialization Enhancement: A Key Innovation for Chest X-ray Report Generation
In Stage 1, we introduce factuality-guided contrastive learning for visual representation by maximizing the semantic correspondence between radiographs and corresponding factual descriptions.
Region-specific Risk Quantification for Interpretable Prognosis of COVID-19
The COVID-19 pandemic has strained global public health, necessitating accurate diagnosis and intervention to control disease spread and reduce mortality rates.
SMC-UDA: Structure-Modal Constraint for Unsupervised Cross-Domain Renal Segmentation
Medical image segmentation based on deep learning often fails when deployed on images from a different domain.
Self-aware and Cross-sample Prototypical Learning for Semi-supervised Medical Image Segmentation
To address these issues, we propose a self-aware and cross-sample prototypical learning method (SCP-Net) to enhance the diversity of prediction in consistency learning by utilizing a broader range of semantic information derived from multiple inputs.
Cross-supervised Dual Classifiers for Semi-supervised Medical Image Segmentation
This paper proposes a cross-supervised learning framework based on dual classifiers (DC-Net), including an evidential classifier and a vanilla classifier.
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization
We investigated uncertainty sampling, annotation redundancy restriction, and initial dataset selection techniques.
Deep Clustering Survival Machines with Interpretable Expert Distributions
We learn weights of the expert distributions for individual instances according to their features discriminatively such that each instance's survival information can be characterized by a weighted combination of the learned constant expert distributions.
Mutual- and Self- Prototype Alignment for Semi-supervised Medical Image Segmentation
In specific, mutual-prototype alignment enhances the information interaction between labeled and unlabeled data.
Cascade Graph Neural Networks for RGB-D Salient Object Detection
Current works either simply distill prior knowledge from the corresponding depth map for handling the RGB-image or blindly fuse color and geometric information to generate the coarse depth-aware representations, hindering the performance of RGB-D saliency detectors. In this work, we introduceCascade Graph Neural Networks(Cas-Gnn), a unified framework which is capable of comprehensively distilling and reasoning the mutual benefits between these two data sources through a set of cascade graphs, to learn powerful representations for RGB-D salient object detection.
Ranked #6 on
RGB-D Salient Object Detection
on NJU2K
Collaborative Boundary-aware Context Encoding Networks for Error Map Prediction
Further, we propose a context encoding module to utilize the global predictor from the error map to enhance the feature representation and regularize the networks.
Hybrid Graph Neural Networks for Crowd Counting
In this paper, we present a novel network structure called Hybrid Graph Neural Network (HyGnn) which targets to relieve the problem by interweaving the multi-scale features for crowd density as well as its auxiliary task (localization) together and performing joint reasoning over a graph.
Trident Segmentation CNN: A Spatiotemporal Transformation CNN for Punctate White Matter Lesions Segmentation in Preterm Neonates
In this paper, a novel spatiotemporal transformation deep learning method called Trident Segmentation CNN (TS-CNN) is proposed to segment PWML in MR images.
Reconstructing Perceived Images from Brain Activity by Visually-guided Cognitive Representation and Adversarial Learning
In addition, we introduce a novel three-stage learning approach which enables the (cognitive) encoder to gradually distill useful knowledge from the paired (visual) encoder during the learning process.
Refined-Segmentation R-CNN: A Two-stage Convolutional Neural Network for Punctate White Matter Lesion Segmentation in Preterm Infants
In this paper, we construct an efficient two-stage PWML semantic segmentation network based on the characteristics of the lesion, called refined segmentation R-CNN (RS RCNN).
A sparse annotation strategy based on attention-guided active learning for 3D medical image segmentation
3D image segmentation is one of the most important and ubiquitous problems in medical image processing.
An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms
We train the deep encoder-decoder for landmark detection, and combine global landmark configuration with local high-resolution feature responses.
Restricting Greed in Training of Generative Adversarial Network
Generative adversarial network (GAN) has gotten wide re-search interest in the field of deep learning.
