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arXiv Machine Learning

Sharpening the Ensemble: An SSIM-Aligned Residual Refiner for Brain-MRI Inpainting Post-Processing

The paper proposes a lightweight residual refiner that post‑processes the outputs of a two‑model ensemble for brain‑MRI inpainting. By training the refiner with an λ‑weighted combination of α loss and SSIM, the authors achieve a modest but statistically significant SSIM improvement (from 0.8767 to 0.8780 on a held‑out set) without altering MSE. Ablations show that adding a third model or using classical unsharp masking does not yield similar gains, indicating the improvement comes from learned sharpening rather than generic post‑processing.

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arXiv Computer Vision
Oct 12

SADL: A Size-Adaptive Decision Layer for Chronic Stroke Lesion Segmentation in ISLES 2026

SADL is a size‑adaptive decision layer that improves chronic stroke lesion segmentation on T1‑weighted MRI by combining an eleven‑member nnU‑Net ensemble with adaptive thresholding and pruning. The ensemble incorporates dice + top‑K loss, heavy augmentation, per‑lesion volume re‑weighting, stochastic weight averaging, and a cohort‑balanced sampling block with a residual‑encoder backbone. On a 91‑case holdout, SADL achieves Dice 0.7322, lesion‑F1 0.7461, and PR‑AUC 0.8430, with a statistically significant gain in lesion‑F1 (p=0.024) especially for small lesions.

By Marek Wodzinski, Gniewosz Drwiega, Wojciech Szymanski
computer-vision
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arXiv Computer Vision
Sep 11

Pre- and Post-Treatment Brain Metastases Segmentation Using nnU-Net with Post-Processing for BraTS 2026

The paper presents a segmentation pipeline for brain metastases in both pre‑ and post‑treatment cases using a 5‑fold nnU‑Net ResEnc‑L ensemble trained on 1,296 four‑modality cases. A rule‑based post‑processing cascade improves the lesion‑wise Dice similarity coefficient (LW‑DSC) for enhancing tumour, tumour core, whole tumour, and resection cavity sub‑regions, achieving LW‑DSC scores of 0.733, 0.751, 0.713, and 0.549 respectively on the official validation leaderboard. The authors conduct a five‑fold out‑of‑fold analysis to validate the robustness of each post‑processing stage, provide a mechanistic explanation of LW‑DSC behaviour, and report thirteen negative results that challenge common intuitions, with all code released under Apache‑2.0.

By Haobin Liu, Xin Wang
computer-visionbenchmarks
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arXiv Computer Vision
Sep 11

Confidence-Calibrating Regularization for Robust Brain MRI Segmentation Under Domain Shift

The paper introduces CalSAM, a lightweight adaptation framework that fine‑tunes only the mask decoder of the Segment Anything Model (SAM) while keeping its encoders frozen. CalSAM employs a Feature Fisher Information Penalty (FIP) to reduce encoder sensitivity to domain shift and a Confidence Misalignment Penalty (CMP) to curb overconfident voxel‑wise errors. Experiments on cross‑center, scanner‑shift, and motion‑corrupted brain MRI datasets show significant gains in Dice similarity coefficient, Hausdorff distance, and expected calibration error, with only a modest training‑time overhead.

By Behraj Khan, Tahir Qasim Syed, Syed Ahmad Chan Bukhari
computer-vision
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arXiv AI
Sep 25

CATCH: Counterfactual Anatomical Tissue Inpainting with Conditional Haar Diffusion

CATCH is a conditional 3D diffusion model operating in an invertible Haar-wavelet domain designed to inpaint masked regions in T1‑weighted brain MRI with plausible, tumor‑free tissue while preserving observed anatomy. The model’s denoiser uses noisy target coefficients, voided‑image coefficients, and a signed mask, guided by tumor‑excluded wavelet reconstruction and a hole‑focused loss, and hard compositing ensures observed voxels remain unchanged. Experiments on BraTS data show that a weighted mixture of tumor‑derived, irregular‑blob, and ellipsoidal masks yields the best performance, achieving higher SSIM, PSNR, and lower MSE compared to fixed or random augmentation baselines.

By Simon Winther Albertsen, Hjalte Bjoernstrup, Said Djafar Said, Mostafa Mehdipour Ghazi
diffusion
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Hugging Face Trending Papers
Sep 10

Pre- and Post-Treatment Brain Metastases Segmentation Using nnU-Net with Post-Processing for BraTS 2026

The paper presents a pragmatic segmentation pipeline for brain metastases in the BraTS 2026 Task 1, using a 5‑fold nnU-Net ResEnc‑L ensemble trained for 1,000 epochs on 1,296 four‑modality cases. A rule‑based post‑processing cascade tuned for the lesion‑wise Dice similarity coefficient (LW‑DSC) improves performance, achieving LW‑DSC scores of 0.733, 0.751, 0.713, and 0.549 on enhancing tumour, tumour core, whole tumour, and resection cavity, respectively. The authors audit each post‑processing stage with a five‑fold out‑of‑fold analysis, confirm two stages as robust, and provide a mechanistic analysis of LW‑DSC, along with thirteen negative results that challenge common intuitions.

computer-visionbenchmarks
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arXiv Computer Vision
Aug 24

When does fusing hand-crafted knowledge with learned representations pay? A cost-normalized benchmark of stacking, substitution, and interference

arXiv:2608.21098v1 Announce Type: new Abstract: Fusing prior knowledge with data-driven learning is attractive where data is scarce, yet no controlled account says when it helps, is redundant, or har...

By Ahmad AlMughrabi, Albert Clop, Benjamin Busam, Ricardo Marques, Petia Radeva
computer-visionbenchmarks
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