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.
By Nimra Dilawar, Sara Nadeem, Javed Iqbal, Waqas Sultani, Mohsen Ali
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...
By Tengyue Zhang, Ruiwen Ding, Luoting Zhuang, Yuxiao Wu, Erika F. Rodriguez, William Hsu
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...
By Chen Shu, Chengyang Zhang, Boyu Fu, Ting Yin, Wenchuan Zhang, Jie Chen, Yuhao Yi, Hong Bu
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...
By Ivan Martinovi\'c, Josip \v{S}ari\'c, Yuki M. Asano, Sini\v{s}a \v{S}egvi\'c
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...
By Akanksha Singh, Vinod K. Kurmi
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.
By Tiffanie Godelaine, Manon Dausort, Karim El Khoury, Beno\^it G\'erin, Beno\^it Macq, Christophe De Vleeschouwer
arXiv:2607.10851v2 Announce Type: replace
Abstract: Medical image classification models are ideally expected to identify diagnostically relevant regions while making predictions, yet standard classif...
By Tonmoy Hossain, Atiqur Rahman, Farhana Hossain Swarnali, Miaomiao Zhang
arXiv:2609.36429v1 Announce Type: new
Abstract: Predicting gene expression from H&E-stained histology images offers a scalable alternative to costly spatial transcriptomics, yet most existing methods...
By Zijun Gao, Chunbin Gu, Jinxi Xiang, Xiangde Luo, Pheng-Ann Heng
arXiv:2608.30420v1 Announce Type: cross
Abstract: Automating the analysis of whole-slide images has high clinical value, since characterizing cancers requires examining them in detail. Such analysis...
By Tiffanie Godelaine, Maxime Zanella, Karim El Khoury, Benoit Macq, Christophe De Vleeschouwer
arXiv:2607. 10783v1 Announce Type: cross Abstract: Whole-slide images (WSIs) provide rich tissue-level and cellular-level information, but storing and transmitting high-magnification pathology data is resource-intensive.
By Dung Minh Do, Nhat-Thanh Huynh, Duc Minh Huynh, Doanh C. Bui, Khang Nguyen
The study investigates whether pathology foundation models (PFMs) carry center-related biases into whole-slide image (WSI) classification. By training models with increasing class-center correlations and evaluating six PFMs across four datasets and two MIL aggregators, the authors introduce the Area Under the Cramér's V Curve (AUCC) to measure both accuracy and degradation due to spurious correlations. Results reveal that center information propagates to WSI predictions, with robustness varying by PFM and MIL strategy, and that ComBat harmonization does not consistently improve robustness.
By Il\'an Carretero, Pablo Meseguer, Roc\'io del Amor, Valery Naranjo
The paper introduces a concept-guided poisoning unlearning framework that explicitly identifies semantic concepts most strongly linked to a target class or knowledge. For image classification, it uses a Post-hoc Concept Bottleneck Model to locate relevant image regions and creates poisoned samples; for large language models, it aggregates Integrated Gradients over multiple question responses to mask important content and generate poisoned training targets. Experiments across several datasets demonstrate effective unlearning while largely preserving model utility.
By Wenhan Chang, Tianqing Zhu, Heng Xu, Wenjian Liu, Wanlei Zhou