🏥 Medical Imaging¶

🎞️ ECCV2026 · 14 paper notes

📌 Same area in other venues: 📷 CVPR2026 (172) · 🔬 ICLR2026 (88) · 🧪 ICML2026 (28) · 🤖 AAAI2026 (75) · 🧠 NeurIPS2025 (77) · 📹 ICCV2025 (31)

🔥 Top topics: Medical Imaging ×6 · Segmentation ×4 · Alignment/RLHF ×2

A Dual-space Patch-driven Complementary Learning Framework for Semi-supervised Multi-organ Segmentation: DPCL combines rarity- and uncertainty-guided cropping, full-image–patch pseudo-label fusion, and complementary contrastive objectives to reduce confusion between neighboring organs, reaching 68.89% mean Dice on Synapse with two labeled training cases.
A Mechanism-Driven Theory of Phase Transitions in Active Learning: This paper proposes a mechanism-driven theory of phase transitions in active learning: the four components of the PAC generalization bound (empirical risk, distribution discrepancy, model complexity, confidence term) are reinterpreted as dynamically interacting terms, proving that a shift in their dominance along the labeling trajectory is inevitable. Measurable proxy metrics and piecewise regression are used to identify a "data-driven - transition - model-driven" three-phase taxonomy, explaining why representativeness, coverage, and uncertainty strategies excel at different budget stages.
Active View Selection for Tomography via Perturbed Gaussian Ensemble: To address the challenging problem of active X-ray view selection in sparse-view CT, a perturbed Gaussian ensemble framework based on radiative 3DGS is proposed. By randomly perturbing the density parameters of low-density Gaussian primitives to construct a lightweight model ensemble, epistemic uncertainty is quantified using the structural similarity (SSIM) variance of projections under candidate views. The view that maximizes the exposure of geometric artifacts is selected as the next best acquisition angle.
AlphaRad: Grounded Zero-Shot Classification in Chest Radiology via α-Corrected Binary Cross Entropy and Factorized Latent Supervision: AlphaRad turns chest radiology reports into per-image concept supervision and combines negative-sampled α-corrected binary cross entropy with independently supervised latent subspaces, reaching 83.3% average AUROC across 16 classification datasets at 224 resolution while improving spatial grounding.
Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation: AtlasSegFM converts one annotated anatomical atlas into automatic prompts for a frozen segmentation foundation model and learns voxel-wise fusion at test time, raising SegRap Dice from 50.02 for five-click nnInteractive to 69.19 while retaining a dependence on anatomical correspondence between support and query.
Beyond Isolated Scans: Cross-Phase Alignment of Structure and Topology for 3D Medical Pretraining: CAST adds wavelet-based structural alignment and local feature-relation alignment to reconstruction-based pretraining on paired non-contrast and contrast-enhanced CT, reaching 0.9163 Dice on AIIB23 airway segmentation without outperforming every baseline on every downstream task.
Beyond Random Sampling: Distribution-Aware Alignment for Semi-Supervised Medical Image Segmentation: The framework selects annotation candidates using frozen visual features and density-weighted K-Center, then trains with memory-guided Copy-Paste and progressive branch activation, reaching 87.3% DSC on PROMISE with two labeled cases; the gain combines changed sample selection and changed training.
BrainFIBRE: A Foundation Model via Information Decomposition for Brain Microstructure: BrainFIBRE is the first foundation model for brain tissue microstructure. It treats three NODDI-derived microstructural maps (NDI/ODI/FWF) as three modalities. Using "Self-supervised Partial Information Decomposition (SPID) + Counterfactual Candidate Construction (CCC)", it pre-trains a Mixture-of-Experts (MoE) network on 55,000 subjects from the UK Biobank. It decouples the unique, redundant, and synergistic information of the three modalities without labels, achieving state-of-the-art (SOTA) performance across diverse downstream tasks including age, sex, cerebrovascular and neurodegenerative markers, and cognition in multi-ethnic cohorts. In addition, the expert weights are highly interpretable.
BrainRiem: Riemannian Prototype Learning for Source-Free Cross-Site Brain Network Diagnosis: BrainRiem proposes to learn compact brain network prototypes on the Riemannian manifold via bi-level optimization, achieving cross-site functional brain connectivity diagnosis without access to source data. It substantially outperforms existing methods on the ABIDE and REST-meta-MDD multi-site benchmarks, and the learned prototypes exhibit biological interpretability.
Dual-Prior Guided Null-Space Learning with Mixture-of-Splines for Arbitrary Medical Slice Super-Resolution: This work reformulates arbitrary-scale medical slice super-resolution as a constrained inverse problem. By utilizing range-null space decomposition, it achieves a hard measurement consistency constraint. Within the null space, a Mixture-of-Splines (MoS) adaptively assigns different continuity orders based on local anatomical features, strictly guaranteeing that the acquired slices remain unchanged while generating anatomically plausible interpolated details.
From Reconstruction to Decision: A Post-Encoder Plug-in Adapter for Curvilinear Segmentation: This paper proposes PEPA, a lightweight post-encoder plug-and-play adapter. Through two modules—Target-Conditioned Serpentine Upsampling (TCSU) and Target-Adaptive Differentiable Binarization (TADT)—it significantly improves the topological continuity of curvilinear structure segmentation (such as blood vessels and cracks) while keeping the foundation model encoder frozen. With an increase of only 0.26M parameters, it achieves average improvements of +2.6% IoU and +2.8% clDice across five medical and industrial benchmarks.
Histopathology Multi-modal Embedding for Pathology Composed Retrieval: HOMIE proposes a model-agnostic two-stage adaptation framework to transform any generative MLLM into a pathology retrieval expert. In the first stage, pure-text LoRA training is employed to teach the LLM a discriminative metric space (resolving task mismatch). In the second stage, native resolution processing, stain augmentation, and a progressive knowledge curriculum inject pathomorphological priors (resolving domain mismatch). On the newly proposed PCR composed retrieval benchmark, a 2B parameter version significantly outperforms 7B specialized pathology MLLMs and dual-encoder models.
Render-FM: Feedforward Model for Real-time Photorealistic Volumetric Rendering: Render-FM proposes a feedforward model based on an nnU-Net-style 3D U-Net that directly regresses 6D Gaussian Splatting (6DGS) parameters from CT volume data in a single 2.8-second forward pass. This eliminates the per-scan optimization bottleneck (~500x speedup). By introducing Anatomy-Guided Prior Initialization (AGP), it utilizes segmentation masks and transfer functions as structural and appearance priors, bridging the domain gap between natural scene reconstruction and medical volume rendering. It supports real-time interactive rendering (328+ FPS) and organ-composition visualization with zero extra overhead.
TaxoMIL: Taxonomy-Constrained Learning for Hierarchical Whole Slide Image Analysis: TaxoMIL reformulates whole slide pathological image diagnosis as a hierarchical text generation task constrained by clinical taxonomies. Using a dual-head decoder, it simultaneously generates coarse- and fine-grained diagnostic texts. It enforces alignment of visual and label representation spaces with the medical taxonomy tree via hierarchical and image-text alignment losses, comprehensively outperforming existing methods on three WSI datasets: gastric, breast, and prostate.