arXiv:2610.20073v1 Announce Type: new Abstract: Accurate nuclei segmentation underpins quantitative computational pathology, yet remains challenging due to morphological variability, weak boundaries,...
arXiv:2607. 14328v1 Announce Type: cross Abstract: In proton therapy planning, respiratory-gated non-contrast CT (NCCT) is commonly used for lesion segmentation; however, accurate delineation remains challenging due to low lesion-to-background contrast.
The paper proposes a scalable framework that uses cell-level spatial transcriptomics (ST) as weak supervision for nuclei classification in histopathology images. By converting gene expression profiles into cell-type labels and leveraging nucleus masks from ST data, the method automatically generates image-label pairs without manual annotation. Evaluations on unseen tissues show that this approach achieves segmentation and classification performance comparable to conventional supervised methods while requiring fewer manually annotated datasets.
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...
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.
CLARiTy is a vision transformer model that jointly performs multi‑label classification and weakly‑supervised localization of chest X‑ray pathologies. It uses class‑specific tokens to produce attention maps and a SegmentCAM module to refine foreground segmentation with anatomical priors. Trained on NIH ChestXray14 image‑level labels and distilled from a ConvNeXtV2 teacher, CLARiTy achieves competitive classification and strong localization, especially for small pathologies, outperforming prior methods by up to 50.7% relative Macro IoU accuracy.
InstEditSeg is a generative framework that treats medical segmentation as an instruction-driven image editing task. Instead of producing binary masks, it renders a color-coded overlay on the original image guided by textual instructions, leveraging latent diffusion models to align with natural image distributions and reduce domain gaps. The method incorporates a DINOv3 visual encoder and a multi-scale feature pyramid fused into the diffusion U‑Net, and uses a dual‑branch classifier‑free guidance strategy to lower inference cost, achieving competitive accuracy on polyp and skin lesion datasets while improving cross‑domain generalization and multi‑lesion segmentation.
arXiv:2601.03163v2 Announce Type: replace Abstract: Background and Objective: Precise and scalable instance segmentation of cell nuclei is a fundamental prerequisite for computational pathology, yet...
arXiv:2610.09784v1 Announce Type: new Abstract: Reliable liver segmentation in contrast-enhanced MRI is essential for quantitative hepatic assessment, treatment planning, and longitudinal disease mon...
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...
The study evaluates multiple computational methods for detecting dead cells in fluorescence microscopy images, comparing heuristic thresholding, classification with numerical and image features, and anomaly detection techniques. Using a dataset of 258 images and 65,527 cells, the best performance was achieved by deep learning image classifiers (ResNet-18) and traditional numerical classifiers (XGBoost), outperforming manual heuristics and anomaly detection. The results highlight the difficulty of reliably identifying apoptotic cells despite advanced machine learning approaches.
arXiv:2511. 03826v4 Announce Type: replace-cross Abstract: Accurate and efficient registration of whole slide images (WSIs) is essential for high-resolution, nuclei-level analysis in multi-stained tissue slides.