Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation
Authors: Yuan Li, Xiaodan Liang, Zhiting Hu, Eric P. Xing
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our approach achieves the state-of-the-art results on two medical report datasets, generating well-balanced structured sentences with robust coverage of heterogeneous medical report contents. We conduct extensive experiments on two medical image report dataset [8]. Our HRGR-Agent achieves the state-of-the-art performance on both datasets under three kinds of evaluation metrics: automatic metrics such as CIDEr [33], BLEU [25] and ROUGE [20], human evaluation, and detection precision of medical terminologies. |
| Researcher Affiliation | Collaboration | Christy Y. Li Duke University yl558@duke.edu Xiaodan Liang Carnegie Mellon University xiaodan1@cs.cmu.edu Zhiting Hu Carnegie Mellon University zhitingh@cs.cmu.edu Eric P. Xing Petuum, Inc epxing@cs.cmu.edu |
| Pseudocode | No | The paper states 'Detailed policy update algorithm is provides in supplementary materials,' but does not include pseudocode or an algorithm block within the main body of the paper. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its source code for the described methodology, nor does it include a direct link to a code repository. |
| Open Datasets | Yes | First, Indiana University Chest X-Ray Collection (IU X-Ray) [8] is a public dataset consists of 7,470 frontal and lateral-view chest x-ray images paired with their corresponding diagnostic reports. |
| Dataset Splits | Yes | On both datasets, we randomly split the data by patients into training, validation and testing by a ratio of 7:1:2. |
| Hardware Specification | Yes | We implement our model on Py Torch and train on a Ge Force GTX TITAN GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch' as the implementation framework, but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We first train all models with cross entropy loss for 30 epochs with an initial learning rate of 5e-4, and then fine-tune the retrieval policy module and generation module of HRGR-Agent via RL with a fixed learning rate 5e-5 for another 30 epochs. We use 512 as dimension of all hidden states and word embeddings, and batch size 16. We set the maximum number of sentences of a report and maximum number of tokens in a sentence as 18 and 44 for CX-CHR and 7 and 15 for IU X-Ray. |