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Daniel Rubin

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4

NeurIPS Conference 2023 Conference Paper

RaLEs: a Benchmark for Radiology Language Evaluations

  • Juanma Zambrano Chaves
  • Nandita Bhaskhar
  • Maayane Attias
  • Jean-Benoit Delbrouck
  • Daniel Rubin
  • Andreas Loening
  • Curtis Langlotz
  • Akshay Chaudhari

The radiology report is the main form of communication between radiologists and other clinicians. Prior work in natural language processing in radiology reports has shown the value of developing methods tailored for individual tasks such as identifying reports with critical results or disease detection. Meanwhile, English and biomedical natural language understanding benchmarks such as the General Language Understanding and Evaluation as well as Biomedical Language Understanding and Reasoning Benchmark have motivated the development of models that can be easily adapted to address many tasks in those domains. Here, we characterize the radiology report as a distinct domain and introduce RaLEs, the Radiology Language Evaluations, as a benchmark for natural language understanding and generation in radiology. RaLEs is comprised of seven natural language understanding and generation evaluations including the extraction of anatomical and disease entities and their relations, procedure selection, and report summarization. We characterize the performance of models designed for the general, biomedical, clinical and radiology domains across these tasks. We find that advances in the general and biomedical domains do not necessarily translate to radiology, and that improved models from the general domain can perform comparably to smaller clinical-specific models. The limited performance of existing pre-trained models on RaLEs highlights the opportunity to improve domain-specific self-supervised models for natural language processing in radiology. We propose RaLEs as a benchmark to promote and track the development of such domain-specific radiology language models.

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JBHI Journal 2020 Journal Article

Natural Language Generation Model for Mammography Reports Simulation

  • Assaf Hoogi
  • Arjun Mishra
  • Francisco Gimenez
  • Jeffrey Dong
  • Daniel Rubin

Extending the size of labeled corpora of medical reports is a major step towards a successful training of machine learning algorithms. Simulating new text reports is a key solution for reports augmentation, which extends the cohort size. However, text generation in the medical domain is challenging because it needs to preserve both content and style that are typical for real reports, without risking the patients' privacy. In this paper, we present a conditioned LSTM-RNN architecture for simulating realistic mammography reports. We evaluated the performance by analyzing the characteristics of the simulated reports and classifying them into benign and malignant classes. An average classification AUC was calculated over two distinct test sets. A qualitative analysis was also performed in which a masked radiologist classified 0. 75 of the simulated reports as real reports, showing that both the style and content of the simulated reports were similar to real reports. Finally, we compared our RNN-LSTM generative model with Markov Random Fields. The RNN-LSTM provided significantly better and more stable performance than MRFs (p <; 0. 01, Wilcoxon).

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NeurIPS Conference 2017 Conference Paper

Inferring Generative Model Structure with Static Analysis

  • Paroma Varma
  • Bryan He
  • Payal Bajaj
  • Nishith Khandwala
  • Imon Banerjee
  • Daniel Rubin
  • Christopher Ré

Obtaining enough labeled data to robustly train complex discriminative models is a major bottleneck in the machine learning pipeline. A popular solution is combining multiple sources of weak supervision using generative models. The structure of these models affects the quality of the training labels, but is difficult to learn without any ground truth labels. We instead rely on weak supervision sources having some structure by virtue of being encoded programmatically. We present Coral, a paradigm that infers generative model structure by statically analyzing the code for these heuristics, thus significantly reducing the amount of data required to learn structure. We prove that Coral's sample complexity scales quasilinearly with the number of heuristics and number of relations identified, improving over the standard sample complexity, which is exponential in n for learning n-th degree relations. Empirically, Coral matches or outperforms traditional structure learning approaches by up to 3. 81 F1 points. Using Coral to model dependencies instead of assuming independence results in better performance than a fully supervised model by 3. 07 accuracy points when heuristics are used to label radiology data without ground truth labels.

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