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ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
TLDR
A new chest X-rays database, namely ChestX-ray8, is presented, which comprises 108,948 frontal-view X-ray images of 32,717 unique patients with the text-mined eight disease image labels from the associated radiological reports using natural language processing, which is validated using the proposed dataset.
TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-Rays
TLDR
A novel Text-Image Embedding network (TieNet) is proposed for extracting the distinctive image and text representations of chest X-rays and multi-level attention models are integrated into an end-to-end trainable CNN-RNN architecture for highlighting the meaningful text words and image regions.
DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning
TLDR
Using DeepLesion, a universal lesion detector is trained that can find all types of lesions with one unified framework and achieves a sensitivity of 81.1% with five false positives per image.
Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-Scale Lesion Database
TLDR
A triplet network is utilized to learn lesion embeddings with a sequential sampling strategy to depict their hierarchical similarity structure and results show promising qualitative and quantitative results on lesion retrieval, clustering, and classification.
Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation
TLDR
A deep stacked transformation approach for domain generalization that can be generalized to the design of highly robust deep segmentation models for clinical deployment and reaches the performance of state-of theart fully supervised models that are trained and tested on their source domains.
When Radiology Report Generation Meets Knowledge Graph
TLDR
Experimental results demonstrate the superior performance of the methods integrated with the proposed graph embedding module on a publicly accessible dataset (IU-RR) of chest radiographs compared with previous approaches using both the conventional evaluation metrics commonly adopted for image captioning and the proposed ones.
NegBio: a high-performance tool for negation and uncertainty detection in radiology reports
TLDR
Evaluation on four datasets demonstrates that NegBio is highly accurate for detecting negative and uncertain findings and compares favorably to a widely-used state-of-the-art system NegEx.
Attention-Guided Curriculum Learning for Weakly Supervised Classification and Localization of Thoracic Diseases on Chest Radiographs
TLDR
A convolutional neural network (CNN) based attention-guided curriculum learning (AGCL) framework is presented, which leverages the severity-level attributes mined from radiology reports to improve the classification and localization performance of thoracic diseases from chest radiographs.
Unsupervised Joint Mining of Deep Features and Image Labels for Large-Scale Radiology Image Categorization and Scene Recognition
TLDR
This work presents a looped deep pseudo-task optimization (LDPO) framework for joint mining of deep CNN features and image labels that is conceptually simple and rests upon the hypothesized "convergence" of better labels leading to better trained CNN models which in turn feed more discriminative image representations to facilitate more meaningful clusters/labels.
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