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Multimodal models show mixed results classifying breast ultrasound images

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv evaluates the performance of multimodal decision models, specifically CLEF and CLEF-Flash, in classifying breast ultrasound images. The research utilized three datasets (Dataset B, BUSI, and BUS-BRA) and tested various prompts to assess accuracy, F1 scores, and latency. CLEF-Flash with a clinical prompt showed the highest accuracy across datasets, though CLEF with a task-guided prompt performed better on specific metrics for the BUSI dataset. The study highlighted significant dataset and prompt dependencies, noting frequent benign-to-malignant misclassifications, particularly on the BUS-BRA dataset, where performance often fell below that of a simple majority-class predictor. AI

IMPACT Highlights the need for careful evaluation of multimodal models in medical imaging, as performance is highly dependent on the dataset and prompt used.

RANK_REASON Academic paper evaluating multimodal models on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Multimodal models show mixed results classifying breast ultrasound images

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Academic paper evaluating multimodal models on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Tran Cao Minh, Hai Tran, Ha Minh Tan ·

    Can Multimodal Decision Models Classify Breast Ultrasound Images? Benchmarking CLEF and CLEF-Flash Across Prompts and Datasets

    arXiv:2610.13675v1 Announce Type: cross Abstract: Multimodal decision models return probabilities over predefined alternatives, offering a structured interface for image classification. This study benchmarks inference with CLEF and CLEF-Flash on 2818 breast ultrasound images from…

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