arXiv Computer Vision
NucShare: A Shared-Decoder Framework for Lightweight Nuclei Instance Segmentation
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LightMIS: Ultra-Lightweight Medical Image Segmentation Without a Stage-Wise Decoder
arXiv:2609.28327v1 Announce Type: new Abstract: We present LightMIS, a scalable family of ultra-lightweight convolutional networks for 2D binary medical image segmentation without a learned stage-wis...
Hugging Face Trending Papers
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DistillPath: An Efficient 22M Distilled Pathology Encoder Approaching Large Foundation Model Performance
DistillPath-KS16 is a 22‑million‑parameter ViT‑S/16 pathology encoder distilled from larger teachers ranging from 86 M to 1.1 B parameters. By training only on the teachers’ final class and patch tokens across 6,000 public slides, it avoids costly pretraining heads and large tile corpora, yet surpasses the kaiko baseline on EVA, HEST, and PLISM benchmarks. The strongest variant, DistillPath-KS16‑Virchow2, achieves a mean EVA score of 0.795—just 0.015 points below the top model—while being 29× smaller and 25× faster.
arXiv AI
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DAMamba-UNet3D: A Parameter-Efficient Mamba State Space U-Net with Dynamic Adaptive Scan for 3D Medical Image Segmentation
arXiv:2607. 22718v1 Announce Type: cross Abstract: We propose parameter-efficient SSM-based U-Net architectures for 3D medical image segmentation.
arXiv AI
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AMN: An Adaptive Multi-Scale Fusion Network with Boundary and Uncertainty Modeling for Nuclei Segmentation
arXiv:2606. 07633v1 Announce Type: cross Abstract: Accurate classification of nuclei subtypes in histopathology images is critical for downstream tasks including tumor grading, immune infiltrate quantification, and prognosis prediction.
arXiv Computer Vision
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TileShare: Training-Free Acceleration of Overlapping Tiled ViT Decoding
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arXiv Computer Vision
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DCM-SAM: Defect-Conditioned Mixture of LoRA Experts for NPU-Deployed AM Defect Segmentation
arXiv:2609.38811v1 Announce Type: new Abstract: Metal additive manufacturing parts are inspected by X-ray computed tomography, where labelled data is scarce, the pores and inclusions that matter span...