Contrastive Learning with Counterfactual Explanations for Radiology Report Generation

• URL: http://arxiv.org/abs/2407.14474v1
• Date: Fri, 19 Jul 2024 17:24:25 GMT
• Title: Contrastive Learning with Counterfactual Explanations for Radiology Report Generation
• Authors: Mingjie Li, Haokun Lin, Liang Qiu, Xiaodan Liang, Ling Chen, Abdulmotaleb Elsaddik, Xiaojun Chang,
• Abstract summary: We propose a textbfCountertextbfFactual textbfExplanations-based framework (CoFE) for radiology report generation. Counterfactual explanations serve as a potent tool for understanding how decisions made by algorithms can be changed by asking what if'' scenarios. Experiments on two benchmarks demonstrate that leveraging the counterfactual explanations enables CoFE to generate semantically coherent and factually complete reports.
• Score: 83.30609465252441
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Due to the common content of anatomy, radiology images with their corresponding reports exhibit high similarity. Such inherent data bias can predispose automatic report generation models to learn entangled and spurious representations resulting in misdiagnostic reports. To tackle these, we propose a novel \textbf{Co}unter\textbf{F}actual \textbf{E}xplanations-based framework (CoFE) for radiology report generation. Counterfactual explanations serve as a potent tool for understanding how decisions made by algorithms can be changed by asking ``what if'' scenarios. By leveraging this concept, CoFE can learn non-spurious visual representations by contrasting the representations between factual and counterfactual images. Specifically, we derive counterfactual images by swapping a patch between positive and negative samples until a predicted diagnosis shift occurs. Here, positive and negative samples are the most semantically similar but have different diagnosis labels. Additionally, CoFE employs a learnable prompt to efficiently fine-tune the pre-trained large language model, encapsulating both factual and counterfactual content to provide a more generalizable prompt representation. Extensive experiments on two benchmarks demonstrate that leveraging the counterfactual explanations enables CoFE to generate semantically coherent and factually complete reports and outperform in terms of language generation and clinical efficacy metrics.

Related papers

• Factual Serialization Enhancement: A Key Innovation for Chest X-ray Report Generation [15.82363717056198]
  A radiology report comprises presentation-style vocabulary, which ensures clarity and organization. A critical step in this process is to align radiographs with their corresponding reports. To address this issue, we propose FSE, a two-stage Factual Serialization Enhancement method.
  arXiv  Detail & Related papers  (2024-05-15T07:56:38Z)
• MedCycle: Unpaired Medical Report Generation via Cycle-Consistency [11.190146577567548]
  We introduce an innovative approach that eliminates the need for consistent labeling schemas. This approach is based on cycle-consistent mapping functions that transform image embeddings into report embeddings. It outperforms state-of-the-art results in unpaired chest X-ray report generation, demonstrating improvements in both language and clinical metrics.
  arXiv  Detail & Related papers  (2024-03-20T09:40:11Z)
• C^2M-DoT: Cross-modal consistent multi-view medical report generation with domain transfer network [67.97926983664676]
  We propose a cross-modal consistent multi-view medical report generation with a domain transfer network (C2M-DoT) C2M-DoT substantially outperforms state-of-the-art baselines in all metrics.
  arXiv  Detail & Related papers  (2023-10-09T02:31:36Z)
• Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models [49.95603725998561]
  We propose a new paradigm to build robust and interpretable medical image classifiers with natural language concepts. Specifically, we first query clinical concepts from GPT-4, then transform latent image features into explicit concepts with a vision-language model.
  arXiv  Detail & Related papers  (2023-10-04T21:57:09Z)
• Cross-modal Clinical Graph Transformer for Ophthalmic Report Generation [116.87918100031153]
  We propose a Cross-modal clinical Graph Transformer (CGT) for ophthalmic report generation (ORG) CGT injects clinical relation triples into the visual features as prior knowledge to drive the decoding procedure. Experiments on the large-scale FFA-IR benchmark demonstrate that the proposed CGT is able to outperform previous benchmark methods.
  arXiv  Detail & Related papers  (2022-06-04T13:16:30Z)
• Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation [3.3978173451092437]
  Radiology report generation aims at generating descriptive text from radiology images automatically. A typical setting consists of training encoder-decoder models on image-report pairs with a cross entropy loss. We propose a novel weakly supervised contrastive loss for medical report generation.
  arXiv  Detail & Related papers  (2021-09-25T00:06:23Z)
• Variational Topic Inference for Chest X-Ray Report Generation [102.04931207504173]
  Report generation for medical imaging promises to reduce workload and assist diagnosis in clinical practice. Recent work has shown that deep learning models can successfully caption natural images. We propose variational topic inference for automatic report generation.
  arXiv  Detail & Related papers  (2021-07-15T13:34:38Z)
• Deep Co-Attention Network for Multi-View Subspace Learning [73.3450258002607]
  We propose a deep co-attention network for multi-view subspace learning. It aims to extract both the common information and the complementary information in an adversarial setting. In particular, it uses a novel cross reconstruction loss and leverages the label information to guide the construction of the latent representation.
  arXiv  Detail & Related papers  (2021-02-15T18:46:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.