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...
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:2607. 22718v1 Announce Type: cross Abstract: We propose parameter-efficient SSM-based U-Net architectures for 3D medical image 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:2610.32233v1 Announce Type: new Abstract: Fixed-window Vision Transformer (ViT) decoders run at deployment resolution as overlapping tiles: every token in an overlap band is processed by severa...
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...
arXiv:2610.33081v1 Announce Type: new Abstract: Generalist segmenters such as Cellpose-SAM (CPSAM) outline cells accurately across stains but slowly; watershed detectors such as QuPath's are fast but...
The paper introduces DEX, an MSDF (most‑significant‑digit‑first) accelerator for quantized U‑Net brain‑tumor segmentation. It employs a two‑stage grouped processing element that supports signed INT8 operands and in‑stream bias accumulation, and implements four runtime mechanisms—exact early negative detection, exact sign‑only decision making, calibrated low‑order‑digit skipping, and calibrated pruning—to dynamically reduce computation. In experiments on a BraTS‑trained residual U‑Net, the approach cuts digit cycles by 38.38 % while maintaining a mean Dice score of 80.58 % (vs. 81.20 % for the floating‑point model), and the exact mechanisms alone achieve an 18.79 % cycle reduction without altering the quantized output.
TAP-Path is a task‑adaptive compression framework that restructures a pretrained Virchow2 encoder for histopathology. It selectively removes transformer blocks, prunes patch tokens, and adds a lightweight gated task head, reducing parameters by 24.96% and FLOPs by 35.20% while maintaining high accuracy on a 32‑class benchmark. The method achieves competitive test metrics and improved rare‑class performance, with strong external validation on CPTAC samples.
Kairos is a byte‑level foundation model that integrates bidirectional gated‑DeltaNet linear attention, sliding‑window attention, block‑wise Attention‑Residual aggregation, a sparse Mixture‑of‑Experts feed‑forward layer, and a multi‑scale byte‑level codec. It is trained with a uniform‑noise denoising objective over a single byte space shared by text, image, audio, video, lidar, and control streams, and the authors provide code‑derived algorithms for each component, including a cross‑session recurrent‑state memory gate. Convergence sanity checks on a 440‑byte text sentence show that the two attention variants reach a minimum loss near 10⁻³, and the implementation appears to optimize, though generalization and multimodal performance remain to be evaluated in future pretraining runs.
DALE-CT introduces depth‑aware 2D slice encoders that learn an anatomical world model of chest CT scans without 3D or positional supervision. By sampling self‑supervised views across a physical $z$‑axis slab, the encoder captures how anatomy changes between neighboring slices, enabling it to recover slice ordering and distinguish slices by anatomy alone. The model, trained on a large 287k‑scan corpus, achieves state‑of‑the‑art performance on CT‑RATE and is released with full code and evaluation tools.
Task-Performance Routing (TPR) is a method for multi-teacher knowledge distillation in CT organ segmentation that assigns teacher weights based on region-wise segmentation errors, favoring teachers with lower errors in hard regions and using uniform weights in easy regions. It employs a hierarchical spatial partition to provide fine-grained routing in shallow encoder stages and coarse-grained routing in deeper stages. Experiments on BTCV, MSD-Liver, and MSD-Pancreas datasets demonstrate that TPR outperforms individual teachers, adaptive ensembles, and other multi-teacher distillation baselines, achieving Dice scores of 65.40% with SwinUNETR and 68.25% with U-Net on MSD-Pancreas.