Hierarchical Section Matching Prediction (HSMP) BERT for Fine-Grained Extraction of Structured Data from Hebrew Free-Text Radiology Reports in Crohn's Disease
What it is about
The authors adapt a Hebrew RoBERTa model to 9,683 Crohn's disease radiology reports, using a masked-language objective and a second objective that predicts whether a Findings section and an Impression section belong to the same report. The model then answers prompts of the form "There is {finding} in the {organ}" for each organ–finding pair. At inference it asks top-down questions (scan normal? organ abnormal? finding present?), and a negative answer skips the questions below it. On 512 manually annotated reports, with 24 labels that have more than 15 positives, the mean F1 is 0.83. The zero-shot version scores 0.49 and standard fine-tuning 0.30. The hierarchical scheme runs 5.1 times faster than the flat one in total time, and the paper uses it to report co-occurrence patterns over the whole corpus.
Ideas
- I1. Top-down pruning for extraction: ask the coarse question first and skip the subtree when it is answered no. For a frame extractor in
prag, a coarse check (does this episode contain any motion event?) can run before the argument-level rules. Measure the number of rule checks saved and the recall lost. - I2. A consistency test between a summary and its source. The paper uses the match between Findings and Impression as a training signal. Mova does not need a learned model for this: a rule that checks whether each fact in a summary has a matching event in the body can flag contradictions in the
worldof a text. - I3. Report the per-label kappa and the label prevalence, not only accuracy. The paper's zero-shot model has accuracy 0.65 but kappa 0.06 on the same labels, so accuracy alone hides the failure. Any extraction test in Mova should show precision, recall, kappa and the count of positives for each relation.
For Mova
I1 belongs to world (the order in which an extractor asks its questions) and to prag (the frame rules). I2 is a test for world. I3 is the reporting standard for every extraction test in prag.
Cite
@misc{turner2025hierarchical,
title = {{Hierarchical Section Matching Prediction (HSMP) BERT for Fine-Grained Extraction of Structured Data from Hebrew Free-Text Radiology Reports in Crohn's Disease}},
author = {Turner;, Zvi Badash; Hadas Ben-Atya; Naama Gavrielov; Liam Hazan; Gili Focht; Ruth Cytter-Kuint; Talar Hagopian; Dan and Freiman, Moti},
year = {2025},
eprint = {2509.04519},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2509.04519}
}References
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Label Ileum Wall Thickness Ileum Inflammation Ileum Wall Enhancement Ileum Stenosis Ileum Pre Stenotic Dilatation Colon Inflammation Ileum DWI Signal Ileum Comb sign Sigmoid Inflammation Colon Wall Thickness Ileum Mesenteric Edema Sigmoid Wall Thickness Colon Wall Enhancement Rectum Inflammation Sigmoid Wall Enhancement Ileum Fistula Cecum Inflammation Rectum Wall Thickness Cecum Wall Thickness Cecum Wall Enhancement Rectum Wall Enhancement Ileum Reduced Motility Sigmoid Comb sign Sigmoid Mesenteric Edema Prevalence 45.2% (215/476) 41.8% (199/476) 34.7% (165/476) 24.2% (115/476) 17.9% (85/476) 15.5% (74/476) 14.7% (70/476) 14.5% (69/476) 13.7% (65/476) 13.4% (64/476) 12.8% (61/476) 11.6% (55/476) 8.8% (42/476) 8.0% (38/476) 7.4% (35/476) 7.1% (34/476) 7.1% (34/476) 6.7% (32/476) 6.7% (32/476) 4.8% (23/476) 4.4% (21/476) 3.4% (16/476) 3.4% (16/476) 3.2% (15/476) Table S2: Performance metrics of the SMP-BERT model across the 24 organ–finding combinations. NPV = negative predictive value, PPV = positive predictive value. Label Accuracy F1 Score Cecum Inflammation Cecum Wall Enhancement Cecum Wall Thickness Colon Inflammation Colon Wall Enhancement Colon Wall Thickness Ileum Comb sign Ileum DWI Signal Ileum Fistula Ileum Inflammation Ileum Mesenteric Edema Ileum Pre Stenotic Dilatation Ileum Reduced Motility Ileum Stenosis Ileum Wall Enhancement Ileum Wall Thickness Rectum Inflammation Rectum Wall Enhancement Rectum Wall Thickness Sigmoid Comb sign Sigmoid Inflammation Sigmoid Mesenteric Edema Sigmoid Wall Enhancement Sigmoid Wall Thickness 0.96 0.97 0.96 0.94 0.94 0.93 0.91 0.97 0.98 0.87 0.84 0.94 0.98 0.95 0.94 0.92 0.94 0.96 0.97 0.97 0.87 0.97 0.94 0.89 Cohen’s κ Balanced Accuracy NPV PPV 0.84 0.83 0.84 0.88 0.79 0.85 0.80 0.93 0.93 0.87 0.65 0.91 0.78 0.93 0.93 0.92 0.83 0.80 0.88 0.63 0.75 0.74 0.77 0.74 0.67 0.65 0.68 0.77 0.58 0.69 0.60 0.87 0.87 0.73 0.30 0.81 0.56 0.87 0.86 0.85 0.66 0.61 0.77 0.27 0.51 0.49 0.54 0.48 0.85 0.78 0.84 0.90 0.77 0.83 0.77 0.91 0.99 0.88 0.65 0.91 0.70 0.95 0.94 0.93 0.89 0.84 0.94 0.60 0.79 0.70 0.76 0.75 0.98 0.98 0.98 0.97 0.96 0.95 0.93 0.97 1.00 0.94 0.91 0.97 0.98 0.98 0.98 0.97 0.99 0.99 0.99 0.97 0.95 0.98 0.97 0.94 0.67 0.80 0.70 0.78 0.67 0.79 0.76 0.95 0.79 0.79 0.38 0.83 1.00 0.86 0.87 0.87 0.59 0.66 0.69 0.52 0.52 0.67 0.60 0.53 Table S3: Performance metrics of the SMP/BERT model when treated as a zero-shot model (no SMP fine-tuning), across the same 24 organ–finding combinations. NPV = negative predictive value, PPV = positive predictive value. Label Accuracy F1 Score Cecum Inflammation Cecum Wall Enhancement Cecum Wall Thickness Colon Inflammation Colon Wall Enhancement Colon Wall Thickness Ileum Comb sign Ileum DWI Signal Ileum Fistula Ileum Inflammation Ileum Mesenteric Edema Ileum Pre Stenotic Dilatation Ileum Reduced Motility Ileum Stenosis Ileum Wall Enhancement Ileum Wall Thickness Rectum Inflammation Rectum Wall Enhancement Rectum Wall Thickness Sigmoid Comb sign Sigmoid Inflammation Sigmoid Mesenteric Edema Sigmoid Wall Enhancement Sigmoid Wall Thickness 0.52 0.61 0.66 0.68 0.68 0.76 0.60 0.49 0.72 0.53 0.63 0.67 0.77 0.59 0.61 0.62 0.59 0.65 0.77 0.61 0.65 0.71 0.65 0.79 Cohen’s κ Balanced Accuracy NPV PPV 0.39 0.42 0.46 0.57 0.52 0.61 0.49 0.44 0.52 0.51 0.48 0.58 0.44 0.51 0.57 0.60 0.38 0.43 0.46 0.42 0.51 0.45 0.47 0.57 -0.01 0.01 0.03 0.18 0.12 0.23 0.06 0.06 0.12 0.02 0.03 0.19 -0.06 0.03 0.15 0.21 -0.11 -0.01 -0.05 0.03 0.07 0.02 0.04 0.15 0.49 0.53 0.54 0.63 0.63 0.65 0.55 0.56 0.64 0.51 0.53 0.62 0.40 0.52 0.58 0.61 0.36 0.48 0.46 0.61 0.56 0.56 0.56 0.59 0.93 0.96 0.94 0.90 0.94 0.91 0.88 0.89 0.96 0.59 0.88 0.87 0.96 0.76 0.71 0.64 0.89 0.95 0.93 0.98 0.93 0.97 0.94 0.91 0.07 0.05 0.08 0.26 0.15 0.29 0.18 0.17 0.13 0.43 0.15 0.29 0.00 0.27 0.44 0.59 0.02 0.04 0.03 0.05 0.17 0.04 0.09 0.22 Table S4: Performance metrics of a standard fine-tune (SFT) BERT model—identical to the HSMP-BERT backbone—across the same 24 organ–finding combinations. NPV = negative predictive value, PPV = positive predictive value. Label Accuracy F1 Score Cecum Inflammation Cecum Wall Enhancement Cecum Wall Thickness Colon Inflammation Colon Wall Enhancement Colon Wall Thickness Ileum Comb sign Ileum DWI Signal Ileum Fistula Ileum Inflammation Ileum Mesenteric Edema Ileum Pre Stenotic Dilatation Ileum Reduced Motility Ileum Stenosis Ileum Wall Enhancement Ileum Wall Thickness Rectum Inflammation Rectum Wall Enhancement Rectum Wall Thickness Sigmoid Comb sign Sigmoid Inflammation Sigmoid Mesenteric Edema Sigmoid Wall Enhancement Sigmoid Wall Thickness 0.93 0.96 0.94 0.89 0.92 0.89 0.87 0.92 0.93 0.88 0.87 0.90 0.97 0.91 0.91 0.91 0.91 0.96 0.93 0.97 0.90 0.97 0.93 0.89 Cohen’s κ Balanced Accuracy NPV PPV 0.00 0.00 0.00 0.53 0.25 0.45 0.41 0.67 0.15 0.87 0.09 0.70 0.00 0.82 0.87 0.90 0.00 0.00 0.00 0.00 0.38 0.00 0.00 0.10 0.00 0.00 0.00 0.47 0.23 0.40 0.35 0.63 0.14 0.76 0.06 0.64 0.00 0.76 0.80 0.81 -0.02 0.00 -0.01 0.00 0.35 0.00 0.00 0.08 0.50 0.50 0.50 0.69 0.57 0.65 0.64 0.76 0.54 0.89 0.52 0.80 0.50 0.88 0.91 0.91 0.49 0.50 0.50 0.50 0.62 0.50 0.50 0.52 0.93 0.96 0.94 0.90 0.92 0.90 0.89 0.92 0.94 0.99 0.88 0.92 0.97 0.94 0.97 0.99 0.92 0.96 0.94 0.97 0.90 0.97 0.93 0.89 0.00 0.00 0.00 0.77 1.00 0.78 0.64 0.92 0.50 0.78 0.33 0.76 0.00 0.82 0.81 0.83 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.50 Figure S1: Each cell represents the pairwise Pearson correlation coefficient between the presence of two findings across patients. Rows and columns are ordered by anatomical location, from proximal (jejunum) to distal (rectum), facilitating interpretation of spatial anatomical patterns. find
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