The paper presents BGD‑SF PolySegNet, a boundary‑guided encoder‑decoder network designed to improve polyp segmentation in colonoscopy images. It introduces three novel modules—BGD‑CMSCA, BG‑SAGF, and MBGH—that explicitly use boundary information to guide dynamic context aggregation, selective feature fusion, and mask refinement. Evaluated on multiple datasets, the model achieves state‑of‑the‑art Dice and boundary metrics while running at 35 FPS with 22.51 M parameters.
arXiv:1812.00877v2 Announce Type: replace-cross Abstract: Segmentation of skin lesion boundaries in dermoscopic imaging is an important prerequisite step for computer-aided diagnosis of malignant mel...
arXiv:2608. 15537v1 Announce Type: cross Abstract: Accurate boundary delineation remains a persistent challenge in dermoscopic image segmentation because of blurred lesion margins, heterogeneous textures, and complex background artifacts.
The paper introduces RABR-Net, a two‑stage framework that refines biomedical image segmentation boundaries by combining multiple uncertainty measures into a boundary‑aware representation. A gated residual refiner uses this representation to selectively correct uncertain boundary pixels while preserving confident regions, leading to modest but statistically significant improvements in Dice, Boundary Dice, and HD95 metrics on a held‑out test set. Qualitative results show the refiner focuses on uncertain cytoplasm and nucleus boundaries, though calibration does not automatically improve.
arXiv:2511. 01143v2 Announce Type: replace-cross Abstract: Early and accurate segmentation of colorectal polyps is critical for reducing colorectal cancer mortality, which has been extensively explored by academia and industry.
arXiv:2608.23745v1 Announce Type: cross Abstract: Accurate brain tumor segmentation from magnetic resonance imaging (MRI) is essential for diagnosis, treatment planning, surgical guidance, and diseas...
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.
Automated segmentation of post-treatment glioma from MRI is essential for accurate diagnosis and treatment planning, yet remains difficult. Surgery and chemoradiotherapy cause structural changes in br...
arXiv:2611.05766v1 Announce Type: new Abstract: Automated segmentation of post-treatment glioma from MRI is essential for accurate diagnosis and treatment planning, yet remains difficult. Surgery and...
arXiv:2604.27697v2 Announce Type: replace-cross Abstract: Peritoneal metastases (PM) are staged using the surgically determined Peritoneal Cancer Index (sPCI), which requires invasive laparoscopic as...
arXiv:2607. 10188v1 Announce Type: cross Abstract: Breast cancer remains the most commonly diagnosed malignancy among women worldwide, yet accurate detection and characterization of breast masses in mammography remain challenging due to subtle intensity variations, heterogeneous tissue densities, and indistinct lesion boundaries that complicate radiological interpretation.
The paper presents a lightweight, energy‑efficient lung tumor segmentation model that combines the ENet architecture with UNet‑style skip connections and a lightweight Convolutional Block Attention Module (CBAM). Trained on TCIA data and evaluated on LIDC‑IDRI, the model achieves a Dice score of 0.90812 while using only 430,052 parameters and 0.549 GMAC, making it up to 10 times faster and far less computationally demanding than UNet or SegNet. Grad‑CAM is employed to provide visual interpretability and highlight areas needing improvement.
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.