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AdaptiveCDM is a modular framework for source‑free few‑shot domain adaptation in cell detection, enabling a pretrained model to adapt to new imaging domains using only a handful of labeled target images and no source data. It combines Resolution‑Aware Augmentation (RAug) to balance scarce, class‑imbalanced samples while preserving cellular morphology, and Category‑Aware Representation Learning (CARL) to strengthen class‑consistent proposals for better localization and classification. Experiments on M5 and Raabin‑WBC datasets show that AdaptiveCDM achieves competitive or superior mAP scores compared to state‑of‑the‑art methods under their respective supervision settings.
arXiv:2601.17228v2 Announce Type: replace Abstract: Deep learning models in computational pathology often fail to generalize across cohorts and institutions due to domain shift. Existing approaches e...
arXiv:2610.15669v2 Announce Type: replace-cross Abstract: Weak bag-level labels are widely available in computational pathology, whereas reliable instance-level annotations for whole-slide images rem...
arXiv:2609.39681v1 Announce Type: new Abstract: Unsupervised domain adaptation (UDA) reduces the annotation burden in panoptic segmentation by leveraging a cost-effectively labeled source domain (e.g...
arXiv:2609.17790v1 Announce Type: new Abstract: Pre-trained vision-language models (VLMs) exhibit strong cross-domain recognition performance even without additional training. However, this robustnes...
The paper introduces SlideTIM, a transductive few‑shot classification method tailored for whole‑slide images (WSIs). SlideTIM extends the LC‑TIM approach by adding a spatial‑latent regularizer and a class‑distribution prior, ensuring that spatially and semantically similar patches receive consistent predictions and that predicted class proportions are calibrated. Experiments on four histology datasets show that SlideTIM outperforms existing TIM variants, boosting macro‑F1 scores by up to 8.1 percentage points over the best baseline and 19.4 percentage points over zero‑shot predictions at one shot.