Public articles linked to the same research event.
arXiv The work proposes Hypothesis-Driven Test-Time Adaptation (HD-TTA): with a frozen nnU-Net v2 backbone and optimization only over test-sample logits, a Gatekeeper first decides whether a case needs refinement, two competing geometric hypotheses (compact denoising vs. diffuse recovery) are generated in parallel, and a representation-guided selector picks the safest output using intrinsic texture consistency; trained on BraTS 2023 GLI and evaluated with strictly fixed hyperparameters on unseen pediatric (PED) and meningioma (MEN) target domains, HD-TTA keeps Dice comparable while improving safety metrics, reducing HD95 from 70.96 mm to 64.55 mm (about 6.4 mm) and raising precision from 15.36% to 19.64% on MEN relative to the strongest baseline TCA.
The work proposes Hypothesis-Driven Test-Time Adaptation (HD-TTA): with a frozen nnU-Net v2 backbone and optimization only over test-sample logits, a Gatekeeper first decides whether a case needs refinement, two competing geometric hypotheses (compact denoising vs. diffuse recovery) are generated in parallel, and a representation-guided selector picks the safest output using intrinsic texture consistency; trained on BraTS 2023 GLI and evaluated with strictly fixed hyperparameters on unseen pediatric (PED) and meningioma (MEN) target domains, HD-TTA keeps Dice comparable while improving safety metrics, reducing HD95 from 70.96 mm to 64.55 mm (about 6.4 mm) and raising precision from 15.36% to 19.64% on MEN relative to the strongest baseline TCA.
The work proposes Hypothesis-Driven Test-Time Adaptation (HD-TTA): with a frozen nnU-Net v2 backbone and optimization only over test-sample logits, a Gatekeeper first decides whether a case needs refinement, two competing geometric hypotheses (compact denoising vs. diffuse recovery) are generated in parallel, and a representation-guided selector picks the safest output using intrinsic texture consistency; trained on BraTS 2023 GLI and evaluated with strictly fixed hyperparameters on unseen pediatric (PED) and meningioma (MEN) target domains, HD-TTA keeps Dice comparable while improving safety metrics, reducing HD95 from 70.96 mm to 64.55 mm (about 6.4 mm) and raising precision from 15.36% to 19.64% on MEN relative to the strongest baseline TCA.
The work proposes Hypothesis-Driven Test-Time Adaptation (HD-TTA): with a frozen nnU-Net v2 backbone and optimization only over test-sample logits, a Gatekeeper first decides whether a case needs refinement, two competing geometric hypotheses (compact denoising vs. diffuse recovery) are generated in parallel, and a representation-guided selector picks the safest output using intrinsic texture consistency; trained on BraTS 2023 GLI and evaluated with strictly fixed hyperparameters on unseen pediatric (PED) and meningioma (MEN) target domains, HD-TTA keeps Dice comparable while improving safety metrics, reducing HD95 from 70.96 mm to 64.55 mm (about 6.4 mm) and raising precision from 15.36% to 19.64% on MEN relative to the strongest baseline TCA.