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.
By Sakib Mohammad, Jarin Ritu, Md Sakhawat Hossain
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.
By Akshat G, Divyansh Gupta, Shaleen Bhatnagar, Shilpa Ankalaki, Tusar Kanti Mishra
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.
By Sofi\`ene Boutaj, Pierre Marza, Varun Belagali, Dimitris Samaras, Maria Vakalopoulou, Stergios Christodoulidis
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.
By Mingxi Fu, Jiawen Li, Renao Yan, Jiali Hu, Qiehe Sun, Tian Guan, Yonghong He
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.
By Phillip Chlap, Mark Lee, Trevor Leong, Matthew Field, Jason Dowling, Hang Min, Julie Chu, Jennifer Tan, Phillip K. Tran, Tomas Kron, Annette Haworth, Martin A. Ebert, Shalini K. Vinod, Lois Holloway