Covers: theory of Natural Language Generation
Estimated time needed to finish: 6 minutes
Questions this item addresses:
  • How does BART contribute to the fine-tuning of the Language Model within Noorallahzadeh et al.'s framework?
How to use this item?

BART is a self-supervised auto-encoder that first uses a noise-added source text as input and later uses a LM for reconstructing the original text by predicting the true replacement of corrupted tokens. It helped us to understand how BART was employed to fine-tune the Language Model on the target domain as part of Noorallahzadeh et al.'s "Progressive Transformer-Based Generation of Radiology Reports".

Author(s) / creator(s) / reference(s)
Rishabh Tripathi
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Understanding the Paper: Progressive Transformer-Based Generation of Radiology Reports

Contributors
Total time needed: ~3 hours
Objectives
Learn what goes into building a multi-stage transformer model by exploring three fundamental topics: Feature Extraction, Attention Mechanism and Visual Language Models.
Potential Use Cases
Clinical Data Mining, Automated & Reproducible Medical Diagnosis, Coherent Report Generation from Images
Who is This For ?
INTERMEDIATENLP or Computer Vision Specialists or Enthusiasts
Click on each of the following annotated items to see details.
Resource Asset7/10
WRITEUP 1. Introduction: Supporting Concepts to Understand Transformer-Based Report Generation
  • Why does the recipe for "Understanding the Paper: Progressive Transformer-Based Generation of Radiology Reports" have the three supporting concept assets sorted in this order?
3 minutes
PAPER 2. An Overview of Image Caption Generation Methods
  • What are the main types of feature extraction methods for images?
  • How may transformers be leveraged to extract features from images?
30 minutes
PAPER 3. When Radiology Report Generation Meets Knowledge Graph
  • How did Noorallahzadeh et al., the authors of our paper of interest "Progressive Transformer-Based Generation of Radiology Reports", construct the training datasets through the usage of MIRQI?
15 minutes
PAPER 4. Attention Is All You Need
  • How do Transformers differ from CNNs and RNNs?
30 minutes
ARTICLE 5. Transformers in Computer Vision: Farewell Convolutions!
  • How can transformers with attention mechanism overcome limitations in convolutional models?
14 minutes
ARTICLE 6. An Overview of ResNet and its Variants
  • What is needed to construct a visual backbone?
15 minutes
PAPER 7. Visual Language Model Content: Generating Radiology Reports via Memory-driven Transformer
  • How was the original idea for the visual language modeling described?
10 minutes
ARTICLE 8. Revealing BART : A denoising objective for pretraining
  • How does BART contribute to the fine-tuning of the Language Model within Noorallahzadeh et al.'s framework?
6 minutes
PAPER 9. Natural Language Generation Content: Generating Radiology Reports via Memory-driven Transformer
  • How were recent radiology report generation conducted using memory-driven transformers?
10 minutes
PAPER 10. Progressive Transformer-Based Generation of Radiology Reports
  • How may image-to-text-to-text be leveraged to generate radiology reports?
25 minutes

Concepts Covered

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