Reviews: Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation

Summary The paper presents an approach using hierarchical reinforcement learning to address the problem of automatically generating medical reports using diagnostics images. The approach first predicts a sequence of hidden states for each sentence, and deicdes when to stop, and a low level model takes the hidden state and either retrieves a sentence and uses it as an output or passes control to a generator which generates a sentence. The overall system is trained with rewards at both sentence level as well as word-level for generation. The proposed approach shows promise over ablations of the proposed model as well as some sensible baseline CNN-RNN based approaches for image captioning. Strengths Paper provides the experimental details of the setup quite thoroughly. Paper clearly mentions the hyperparameters used for training.