arXiv:2606. 16234v1 Announce Type: cross Abstract: Fundus fluorescein angiography (FFA) is critical for assessing retinal vascular abnormalities, but its acquisition is invasive and not always feasible.
arXiv:2607. 16065v1 Announce Type: cross Abstract: Retinal layer segmentation in Optical Coherence Tomography (OCT) is a fundamental step for extracting quantitative biomarkers of retinal structure.
arXiv:2609.24049v1 Announce Type: new Abstract: Accurate retinal vessel segmentation is important for computer-aided ophthalmic analysis, yet thin vessels, low contrast, and severe foreground-backgro...
arXiv:2606. 23825v1 Announce Type: cross Abstract: Efficient small object detection is bottlenecked by the inherent feature scarcity of tiny targets, which is further aggravated by operations of spatial-domain detectors that indiscriminately discard critical high-frequency details.
The paper introduces FreNet, a feature reconfiguration framework that incorporates visual priors for medical lesion segmentation. FreNet performs pixel‑level reconfiguration before encoding using an Implicit Prior Neural Network (IPNN) that leverages SAM, and feature‑level reconfiguration during encoding via a Dual‑domain Feature Reconfiguration (DFR) module, which includes a Frequency Decoupling Module (FDM) and a Spatial Localization Module (SLM). Experiments on nine benchmarks across three imaging modalities show that FreNet outperforms state‑of‑the‑art methods, achieving a 5.0% Dice improvement over the best baseline on the ETIS dataset and a 7.2% improvement over SAM.
OptiModNet is a lightweight UNet‑Transformer hybrid designed for optic disc and cup segmentation. It incorporates grouped‑query and channel attention across multiple stages, along with an Aggregated Pyramid Loss to improve gradient flow and structural consistency. Evaluated on the REFUGE2 dataset, it surpasses existing methods by over 2.5 % while using only 3.73 GFLOPs and 1.93 M parameters.
Feature Reconfiguration With Visual Prior for Medical Lesion Segmentation proposes FreNet, a framework that reconfigures images and features before and during encoding to improve lesion segmentation. It introduces an Implicit Prior Neural Network that uses a visual prior from SAM to suppress background responses, and a Dual-domain Feature Reconfiguration module that decouples features in frequency and spatial domains to better handle diverse lesion morphology. Experiments on nine benchmarks across three imaging modalities show FreNet outperforms state‑of‑the‑art methods, achieving a 5.0% Dice improvement over the best prior method on the ETIS dataset.
arXiv:2607. 03959v1 Announce Type: cross Abstract: Diabetic retinopathy (DR) is a leading cause of vision impairment worldwide, highlighting the need for accurate and accessible screening tools.
MambaMPD is a new segmentation framework that leverages Vision Mamba models for marine pollution detection in remote‑sensing imagery. It introduces two structural priors—Frequency‑Aware Augmentation (FAA) and multi‑scale Edge‑Guided Attention (EGA)—to better capture low‑contrast, fragmented pollution patterns and sharpen boundaries. Experiments on the MADOS and M4D datasets show that MambaMPD outperforms existing methods in mIoU while using far less computation than foundation‑model approaches.
HyperAMS‑Net is a deep learning framework that classifies brain disorders from resting‑state fMRI or structural MRI data. It combines adaptive multi‑scale convolution, hypergraph attention, spatial‑channel attention, and adaptive feature fusion to capture complementary patterns across multiple scales and higher‑order dependencies. Evaluated on ABIDE, REST‑meta‑MDD, and ADNI datasets, it achieves state‑of‑the‑art accuracy and AUC, with ablation studies showing hypergraph attention as the most critical component.
arXiv:2609.12574v1 Announce Type: new Abstract: Ultra-widefield (UWF) swept-source optical coherence tomography angiography (SS-OCTA) enables large-area retinal vascular imaging, yet vessel segmentat...
Accurate coronary Digital Subtraction Angiography (DSA) vessel segmentation is essential for computer-aided diagnosis and treatment planning of coronary artery disease (CAD). However, thin low-contrast vessels, background interference, and severe vessel-background class imbalance make reliable segmentation of weak distal branches and vessel boundaries challenging.