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.
arXiv:2606. 00928v1 Announce Type: cross Abstract: Multiplexed fluorescence microscopy improves tissue segmentation by providing complementary channels including nuclear (DAPI) and membrane (E-cadherin), that together encode richer spatial context than single-channel imaging alone.
The paper introduces an unsupervised approach to medical image segmentation by training a Denoising Diffusion Probabilistic Model (DDPM) on 21 unlabeled abdominal CT scans to learn anatomical features. The encoder weights from the DDPM are transferred to a U‑Net for downstream segmentation on the BTCV multi‑organ dataset, resulting in a significant Dice score improvement for liver segmentation from 0.75 to 0.93. In low‑data regimes, diffusion‑pretrained models retain robust performance, achieving high Dice scores even with only 10% of labeled data.
Acquiring pixel-level annotations for medical image segmentation is a severe bottleneck. Traditional U-Net architectures, while effective, learn local texture patterns and lack awareness of global ana...
The paper introduces a scalable multi-teacher distillation (MTD) framework for digital pathology, addressing the poor performance of standard MTD when many teachers are used. By learning teacher‑specific representations through techniques such as learnable teacher tokens, a tailored attention scheme, mixture‑of‑experts layers, and a contrastive loss, the method scales effectively, enabling compact student encoders that are up to 10× more efficient than larger teacher models while matching or surpassing their performance on 39 tile‑ and slide‑level tasks. The authors release a new collection of efficient foundation models, OMNI, trained from ten state‑of‑the‑art teachers and provide an analysis of the complementary teacher‑specific representations.
arXiv:2607. 14703v1 Announce Type: cross Abstract: Multiple instance learning (MIL) has become the main paradigm for whole-slide image (WSI) analysis in computational pathology.
The study explores how adding anatomical priors and active learning can improve the accuracy of deep learning models for segmenting the Clinical Target Volume (CTV) in gastric cancer radiotherapy. Using 100 retrospective CT scans, an nnU‑Net model trained on 10 expert‑contoured cases was enhanced with voxel‑wise anatomical prior maps and iterative active learning over four rounds. The combined approach raised the mean Dice Similarity Coefficient from 0.84 to 0.87, demonstrating that both techniques individually and together improve segmentation performance and generalizability.
arXiv:2608.30844v1 Announce Type: cross Abstract: Interactive lesion segmentation in whole-body PET/CT requires a model to provide a strong initial prediction while also responding efficiently to spa...
arXiv:2609.06729v2 Announce Type: replace Abstract: Accurate 3D brain tumour segmentation from multi-modal Magnetic Resonance Imaging (MRI) is essential for clinical diagnosis and treatment planning....
Organ segmentation from PET/CT is critical for quantitative analysis and radiotherapy planning in oncology. To ease the high annotation cost of PET/CT segmentation, semi-supervised learning (SSL) provides a practical and effective solution for developing deep models with limited labeled data.
arXiv:2602.20773v2 Announce Type: replace Abstract: Purpose: Developing generalizable medical image segmentation models is challenging because imaging data are distributed across institutions and dif...
arXiv:2609.23815v1 Announce Type: new Abstract: Medical image segmentation models typically rely on large amounts of densely annotated volumetric data, limiting their scalability across tasks and ima...
arXiv:2606. 15611v1 Announce Type: cross Abstract: Organ segmentation from PET/CT is critical for quantitative analysis and radiotherapy planning in oncology.