SOTAVerified

Medical Report Generation

Medical report generation (MRG) is a task which focus on training AI to automatically generate professional report according the input image data. This can help clinicians make faster and more accurate decision since the task itself is both time consuming and error prone even for experienced doctors.

Aggfgg

Deep neural network and transformer based architecture are currently the most popular methods for this certain task, however, when we try to transfer out pre-trained model into this certain domain, their performance always degrade.

The following are some of the reasons why RSG is hard for pre-trained models:

  • Language datasets in a particular domain can sometimes be quite different from the large number of datasets available on the Internet
  • During the fine-tuning phase, datasets in the medical field are often unevenly distributed

More recently, multi-modal learning and contrastive learning have shown some inspiring results in this field, but it's still challenging and requires further attention.

Here are some additional readings to go deeper on the task:

https://arxiv.org/abs/2004.12150

(Image credit : Transformers in Medical Imaging: A Survey)

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–75 of 110 papers

TitleStatusHype
Reinforcement Learning with Imbalanced Dataset for Data-to-Text Medical Report Generation—0
Representative Image Feature Extraction via Contrastive Learning Pretraining for Chest X-ray Report Generation—0
Resource-Efficient Medical Report Generation using Large Language Models—0
Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation—0
Addressing Data Bias Problems for Chest X-ray Image Report Generation—0
A Labeled Ophthalmic Ultrasound Dataset with Medical Report Generation Based on Cross-modal Deep Learning—0
AlignTransformer: Hierarchical Alignment of Visual Regions and Disease Tags for Medical Report Generation—0
A Medical Semantic-Assisted Transformer for Radiographic Report Generation—0
A Self-Boosting Framework for Automated Radiographic Report Generation—0
A Self-Guided Framework for Radiology Report Generation—0
A Survey on Deep Learning and Explainability for Automatic Report Generation from Medical Images—0
A Survey on Trustworthiness in Foundation Models for Medical Image Analysis—0
Auto-Encoding Knowledge Graph for Unsupervised Medical Report Generation—0
Automatic Medical Report Generation: Methods and Applications—0
Boosting Radiology Report Generation by Infusing Comparison Prior—0
Brain Cancer Survival Prediction on Treatment-na ive MRI using Deep Anchor Attention Learning with Vision Transformer—0
C^2M-DoT: Cross-modal consistent multi-view medical report generation with domain transfer network—0
Competence-based Multimodal Curriculum Learning for Medical Report Generation—0
Cross-modal Clinical Graph Transformer for Ophthalmic Report Generation—0
Cross-modal Contrastive Attention Model for Medical Report Generation—0
Customizing General-Purpose Foundation Models for Medical Report Generation—0
Cyclic Generative Adversarial Networks With Congruent Image-Report Generation For Explainable Medical Image Analysis—0
DAMPER: A Dual-Stage Medical Report Generation Framework with Coarse-Grained MeSH Alignment and Fine-Grained Hypergraph Matching—0
DeltaNet: Conditional Medical Report Generation for COVID-19 Diagnosis—0
Dia-LLaMA: Towards Large Language Model-driven CT Report Generation—0
Show:102550
← PrevPage 3 of 5Next →
All datasetsMIMIC-CXRHistGen WSI-Report DatasetIU X-Ray

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RGRGBLEU-137.3—Unverified
2SEI-1BLEU-20.25—Unverified
#ModelMetricClaimedVerifiedStatus
1HistGenBLEU-40.18—Unverified
#ModelMetricClaimedVerifiedStatus
1X-RGenBLEU-40.18—Unverified