Papers by Amilcare Gentili
MedEval: A Multi-Level, Multi-Task, and Multi-Domain Medical Benchmark for Language Model Evaluation (2023.emnlp-main)
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| Challenge: | Existing medical datasets require high quality domain-specific datasets. |
| Approach: | They propose a multi-level, multi-task, and multi-domain medical benchmark to facilitate the development of language models for healthcare. |
| Outcome: | The proposed model provides granular potential usage and supports a wide range of tasks. |
Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation (2021.findings-emnlp)
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| Challenge: | Radiology report generation aims at generating descriptive text from radiology images automatically. |
| Approach: | They propose a weakly supervised contrastive loss method that generates descriptive text from radiology images automatically. |
| Outcome: | The proposed method outperforms previous work on correctness and text generation metrics for two public benchmarks. |
Learning Visual-Semantic Embeddings for Reporting Abnormal Findings on Chest X-rays (2020.findings-emnlp)
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| Challenge: | Existing work on report generation often trains encoder-decoder networks to generate complete reports, but such models are affected by data bias and face common issues inherent in text generation models. |
| Approach: | They propose a method to identify abnormal findings from radiology images and group them with unsupervised clustering and minimal rules. |
| Outcome: | The proposed method outperforms existing generation models on correctness and text generation metrics. |