Public articles linked to the same research event.
Journal of imaging informatics in medicine This work proposes LOCUS-Diff, a generative framework that combines a thyroid ultrasound synthesis foundation model, a nodule spatial control branch, and a COMB label-correction mechanism to produce synthetic samples judged by senior clinical experts in visual Turing tests as anatomically plausible and rivaling real scans, consistently outperforming the state of the art in downstream nodule detection on TN5000 and TN3k, and achieving higher mAP when augmenting a training subset with only 60% of real data with synthetic samples than training on the full real dataset.
This work proposes LOCUS-Diff, a generative framework that combines a thyroid ultrasound synthesis foundation model, a nodule spatial control branch, and a COMB label-correction mechanism to produce synthetic samples judged by senior clinical experts in visual Turing tests as anatomically plausible and rivaling real scans, consistently outperforming the state of the art in downstream nodule detection on TN5000 and TN3k, and achieving higher mAP when augmenting a training subset with only 60% of real data with synthetic samples than training on the full real dataset.
This work proposes LOCUS-Diff, a generative framework that combines a thyroid ultrasound synthesis foundation model, a nodule spatial control branch, and a COMB label-correction mechanism to produce synthetic samples judged by senior clinical experts in visual Turing tests as anatomically plausible and rivaling real scans, consistently outperforming the state of the art in downstream nodule detection on TN5000 and TN3k, and achieving higher mAP when augmenting a training subset with only 60% of real data with synthetic samples than training on the full real dataset.
This work proposes LOCUS-Diff, a generative framework that combines a thyroid ultrasound synthesis foundation model, a nodule spatial control branch, and a COMB label-correction mechanism to produce synthetic samples judged by senior clinical experts in visual Turing tests as anatomically plausible and rivaling real scans, consistently outperforming the state of the art in downstream nodule detection on TN5000 and TN3k, and achieving higher mAP when augmenting a training subset with only 60% of real data with synthetic samples than training on the full real dataset.