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Weighting What Matters: Boosting Sample Efficiency in Medical Report Generation via Token Reweighting

Alexander Weers, Daniel Rueckert, Martin J. Menten · Apr 22, 2026 · Citations: 0

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How to use this page

Provisional trust

This page is a lightweight research summary built from the abstract and metadata while deeper extraction catches up.

Best use

Background context only

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Evidence quality

Provisional

Derived from abstract and metadata only.

Abstract

Training vision-language models (VLMs) for medical report generation is often hindered by the scarcity of high-quality annotated data. This work evaluates the use of a weighted loss function to improve data efficiency. Compared to standard cross-entropy loss, which treats all token prediction errors equally, the reweighted loss shifts the focus to semantically salient tokens with outsized clinical importance. In experiments on ophthalmological report generation, we show that this simple method improves efficiency across multiple data scales, achieving similar report quality with up to ten times less training data.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

  • This page is still relying on abstract and metadata signals, not a fuller protocol read.
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Should You Rely On This Paper?

Signal extraction is still processing. This page currently shows metadata-first guidance until structured protocol fields are ready.

Best use

Background context only

Use if you need

A provisional background reference while structured extraction finishes.

Main weakness

This page is still relying on abstract and metadata signals, not a fuller protocol read.

Trust level

Provisional

Usefulness score

Unavailable

Eval-fit score is unavailable until extraction completes.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

Usefulness for eval research

Provisional (processing)

Extraction confidence 0%

If you are doing eval pipeline work, start here:

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What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

provisional (inferred)

None explicit

No explicit feedback protocol extracted.

"Training vision-language models (VLMs) for medical report generation is often hindered by the scarcity of high-quality annotated data."

Evaluation Modes

provisional (inferred)

None explicit

Validate eval design from full paper text.

"Training vision-language models (VLMs) for medical report generation is often hindered by the scarcity of high-quality annotated data."

Quality Controls

provisional (inferred)

Not reported

No explicit QC controls found.

"Training vision-language models (VLMs) for medical report generation is often hindered by the scarcity of high-quality annotated data."

Benchmarks / Datasets

provisional (inferred)

Not extracted

No benchmark anchors detected.

"Training vision-language models (VLMs) for medical report generation is often hindered by the scarcity of high-quality annotated data."

Reported Metrics

provisional (inferred)

Not extracted

No metric anchors detected.

"Training vision-language models (VLMs) for medical report generation is often hindered by the scarcity of high-quality annotated data."

Rater Population

provisional (inferred)

Unknown

Rater source not explicitly reported.

"Training vision-language models (VLMs) for medical report generation is often hindered by the scarcity of high-quality annotated data."

Human Feedback Details

This page is using abstract-level cues only right now. Treat the signals below as provisional.

  • Potential human-data signal: No explicit human-data keywords detected.
  • Potential benchmark anchors: No benchmark names detected in abstract.
  • Abstract highlights: 3 key sentence(s) extracted below.

Evaluation Details

Evaluation fields are inferred from the abstract only.

  • Potential evaluation modes: No explicit eval keywords detected.
  • Potential metric signals: No metric keywords detected.
  • Confidence: Provisional (metadata-only fallback).

Research Brief

Metadata summary

Training vision-language models (VLMs) for medical report generation is often hindered by the scarcity of high-quality annotated data.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • Training vision-language models (VLMs) for medical report generation is often hindered by the scarcity of high-quality annotated data.
  • This work evaluates the use of a weighted loss function to improve data efficiency.
  • Compared to standard cross-entropy loss, which treats all token prediction errors equally, the reweighted loss shifts the focus to semantically salient tokens with outsized clinical importance.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

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