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Browse filesAdd information about external validation
README.md
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# Agentic Thyroid ResNet-18 — Ultrasound Nodule Malignancy Classifier
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> ⚠️ **RESEARCH USE ONLY — NOT FOR CLINICAL USE.** This model is a research
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> artifact trained on a single retrospective dataset
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> clinical decision-making.
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> any clinical consideration.
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A ResNet-18 binary classifier that predicts the probability that a cropped
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## Limitations, bias, and leakage concerns
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- **Single-source dataset
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other scanners, institutions, or populations is unknown and likely lower.
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- **Cropped-ROI inputs.** The model expects nodule-cropped images like TN5000;
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whole-frame ultrasound will be out of distribution.
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- **Leakage checks were exhaustive within the available signal** (exact pixel
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hashing + filename-ID overlap, all zero) but cannot rule out near-duplicate or
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same-patient-different-image leakage if such structure exists in the source.
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## ⚠️ External validation required before clinical use
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This model **requires independent, ideally prospective, multi-site external
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validation** before any clinical consideration. It is released for research
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reproducibility only.
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# Agentic Thyroid ResNet-18 — Ultrasound Nodule Malignancy Classifier
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> ⚠️ **RESEARCH USE ONLY — NOT FOR CLINICAL USE.** This model is a research
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> artifact trained on a single retrospective dataset and is validated only one external dataset. It
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> **must not** be used for diagnosis, screening, or any
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> clinical decision-making. Multiple external, prospective validation is required before
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> any clinical consideration.
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A ResNet-18 binary classifier that predicts the probability that a cropped
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## Limitations, bias, and leakage concerns
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- **Single-source dataset** Performance on data from
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other scanners, institutions, or populations is unknown and likely lower.
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- **Cropped-ROI inputs.** The model expects nodule-cropped images like TN5000;
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whole-frame ultrasound will be out of distribution.
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- **Leakage checks were exhaustive within the available signal** (exact pixel
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hashing + filename-ID overlap, all zero) but cannot rule out near-duplicate or
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same-patient-different-image leakage if such structure exists in the source.
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But the original article describing the dataset states that "Only one representative image among the images of the same perspective of the patient is reserved, to prevent the risk of data leakage and ensure the dataset can be partitioned at the patient level;"
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## ⚠️ External validation required before clinical use
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At present this model has only one external validation.
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This model **requires independent, ideally prospective, multi-site external
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validation** before any clinical consideration. It is released for research
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reproducibility only.
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