arXiv:2607. 18990v1 Announce Type: cross Abstract: SWITi is a test-time method for reducing artifacts in tiled predictions, particularly for neural networks that learn posterior distributions from which solutions are sampled at inference time.
SWITi is a test-time method for reducing artifacts in tiled predictions, particularly for neural networks that learn posterior distributions from which solutions are sampled at inference time. Tiled predictions are unavoidable for large image data, and artifacts arise whenever tiles are smaller than a network's receptive field and when tiles are independent posterior samples.
The paper introduces the Consistency Memory Bank (COMB), a label‑free virtual staining framework designed to process gigapixel Whole Slide Images without the memory bottlenecks of patch‑based deep learning. COMB decouples context storage from computation, using a dynamic retrieval mechanism to fetch feature representations from adjacent tiles, local padding to resolve spatial discontinuities, and neighbor‑aware channel attention to stabilize statistical drift. The method achieves superior perceptual fidelity and tiling consistency compared to state‑of‑the‑art baselines, and its improved continuity suggests downstream benefits for tumor segmentation.
arXiv:2608.20929v1 Announce Type: new Abstract: AI-generated image manipulation localization identifies edited pixels, but its OOD performance lags behind image-level detection partly because pixel s...
arXiv:2602.19736v3 Announce Type: replace Abstract: Diffusion models now give the best perceptual quality in super-resolution (SR), but their architecture and training confine them to small fixed cro...
arXiv:2609.00396v1 Announce Type: new Abstract: Histopathological whole slide images (WSIs) are central to cancer diagnosis, but their gigapixel scale, tissue heterogeneity, weak slide-level supervis...
arXiv:2603. 17555v2 Announce Type: replace-cross Abstract: Diffusion-based image-to-video (I2V) models are increasingly effective, yet they struggle to scale to ultra-high-resolution inputs (e.
arXiv:2609.24116v1 Announce Type: new Abstract: Although deep learning has advanced Whole Slide Image (WSI) Analysis, tissue artifacts like bubbles and folds often cause silent failures by concealing...
arXiv:2602. 20114v2 Announce Type: replace-cross Abstract: Machine unlearning (MU) refers to the post-training capability to remove (the influence of) training examples that are incorrect, biased, or leak sensitive/private information.
GramLoop is a training‑free framework that enhances frozen DINOv3 dense‑prediction models under distribution shift by adding inference computation within the visual backbone. It replays a short transformer window and uses final‑layer cosine‑Gram consistency to control each replay, propagating proposals through the frozen suffix and accepting them via a patchwise gate. Across object detection and semantic segmentation tasks, GramLoop improves performance on all five shifted benchmarks, notably raising COCO‑O mAP by +0.252 and Effective Robustness by +0.250 while maintaining clean ADE20K accuracy.
arXiv:2608.22066v1 Announce Type: cross Abstract: Attention-based multiple instance learning (ABMIL) using pathology foundation model embeddings is effective for slide-level tasks, but exhaustive inf...
The paper introduces Structural Dual Super‑Resolution (SDN), a novel approach that shifts from pixel‑level super‑resolution to topological inference for trabecular bone morphology. By training on 2‑D slices and evaluating on 3‑D morphological metrics, SDN learns to predict invariant microstructures from low‑resolution CT inputs, using bidirectional modeling, a multi‑scale consistency discriminator, and four structural duality constraints. The method achieves SSIM of 0.8 and morphological parameters closely matching synchrotron micro‑CT across six metrics, demonstrating cross‑source generalization and trustworthy inference rather than mere pixel generation.
arXiv:2608.29677v1 Announce Type: cross Abstract: Despite rapid advances in MIS, fair and reproducible comparisons of segmentation models remain challenging due to heterogeneous datasets, inconsisten...