{"total":14,"items":[{"citing_arxiv_id":"2606.21756","ref_index":14,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Scaling up fine-grained intracranial vessel annotations in computed tomography angiography","primary_cat":"eess.IV","submitted_at":"2026-06-19T21:18:04+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"SemanticVessel is a new CTA vessel segmentation dataset using intensity-guided region growing, expert labeling of 20 arterial classes, and multi-phase label reuse, with reported gains from including a generic minor-artery class.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.18707","ref_index":15,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"PEFT-MedSAM: Efficient Fine-Tuning of Medical Foundation Models for Explainable Skin Lesion Segmentation","primary_cat":"cs.CV","submitted_at":"2026-06-17T05:42:24+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":4.0,"formal_verification":"none","one_line_summary":"PEFT-MedSAM adapts MedSAM by training only its mask decoder on ISIC 2018 skin lesion data, achieving Dice 0.9411 and outperforming U-Net (0.8715) and zero-shot MedSAM (0.8997), with PH2 validation (0.9467) and 98.27% Grad-CAM pointing accuracy.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.14957","ref_index":74,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Learning Sparse Latent Predictive Foundation Model for Multimodal Neuroimaging","primary_cat":"cs.CV","submitted_at":"2026-06-12T21:02:09+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Neuro-JEPA is a sparse multimodal foundation model pretrained on 1,551,862 brain MRI scans that shows stronger and more consistent performance than existing models and CNN baselines across 47 tasks from clinical and public datasets.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.06103","ref_index":15,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"MS-DKC: A Dataset Knowledge Card Framework for Designing and Adapting Medical Image Segmentation Models","primary_cat":"cs.CV","submitted_at":"2026-06-04T12:45:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"MS-DKC is a dataset knowledge card framework that maps image, morphology, supervision, context, and risk descriptors to design priors and failure modes, shown to produce dataset-specific model adaptations with improved metrics on DRIVE, ISIC2018, and ACDC.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16572","ref_index":11,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"TriALS: Triphasic-Aided Liver Lesion Segmentation Benchmark in Non-Contrast CT","primary_cat":"cs.CV","submitted_at":"2026-05-15T19:23:35+00:00","verdict":"ACCEPT","verdict_confidence":"MODERATE","novelty_score":7.0,"formal_verification":"none","one_line_summary":"TriALS introduces a 150-case four-phase CT dataset and challenge showing top segmentation methods reach 0.754 Dice on venous phase but only 0.57 on non-contrast CT, with external validation gains up to 28%.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16393","ref_index":35,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Vision Transformer-Conditioned UNet for Domain-Adaptive Semantic Segmentation","primary_cat":"cs.CV","submitted_at":"2026-05-12T11:56:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"ViTC-UNet adapts frozen ViT representations to biomedical semantic segmentation by conditioning a UNet via learnable tokens and two-way attention decoding.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.03602","ref_index":4,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation","primary_cat":"eess.IV","submitted_at":"2026-05-05T10:27:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"Dante is a new open-source backend for the Dafne ecosystem that implements configurable training from scratch, layer freezing, and channel-wise LoRA for medical image segmentation, with validation showing faster convergence and higher Dice scores in cross-domain MRI tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.25685","ref_index":2,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Robustness Evaluation of a Foundation Segmentation Model Under Simulated Domain Shifts in Abdominal CT: Implications for Health Digital Twin Deployment","primary_cat":"eess.IV","submitted_at":"2026-04-28T14:16:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"SAM (ViT-B) shows stable spleen segmentation in abdominal CT with mean Dice drop below 0.01 and no rise in failures under simulated domain shifts like noise, blur, and contrast changes.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.05594","ref_index":16,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"RABC-Net: Reliability-Aware Annotation-Free Skin Lesion Segmentation for Low-Resource Dermoscopy","primary_cat":"cs.CV","submitted_at":"2026-04-07T08:43:34+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"RABC-Net achieves 86.58% DICE and 79.47% JAC on skin lesion segmentation across ISIC-2017, ISIC-2018, and PH2 using only pseudo-labels and no manual masks for training or adaptation.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"background-preservation, and sparsity losses. With pseudo- consensus map𝑃𝑐, Sobel boundary magnitude𝐵 = (𝑃𝑐), boundaryband ̄𝐵 = MaxPool(𝐵, 𝑘 = 5),supportmap 𝛼,and boundaryweight 𝑊𝑏 = 1+ 𝜆𝑏𝑃𝑐 max(𝐵, 𝑏)(0.5+0.5𝑢)(1−𝑝), we use rabc-bnd = BCElogits (̂ 𝑧, 𝑃𝑐; 𝑊𝑏 ) (14) rabc-far = ∑ReLU(𝜎( ̂ 𝑧) − 𝜎(𝑧) − 𝑚) (1 − 𝑃𝑐)(1 − ̄𝐵) ∑(1 − 𝑃𝑐)(1 − ̄𝐵) + 𝜖 (15) rabc-sp = mean(|Δ𝑧| (1 − 𝛼)) (16) where 𝑚 = 0 .02 and 𝑘 = 5 in all reported RABC experi- ments.TheRABC-specificlossweightsaresetto0.04,0.02, and 0.01 forrabc-bnd, rabc-far, andrabc-sp, respectively. In this sense, RABC is a reliability-aware calibration mecha- nismembeddedwithinthesegmentationnetworkratherthan a fixed post-threshold heuristic. Atinference,thedeployedimagebranchdirectlyoutputs"},{"citing_arxiv_id":"2602.20845","ref_index":10,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"FLIM Networks with Bag of Feature Points","primary_cat":"cs.CV","submitted_at":"2026-02-24T12:36:22+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"FLIM-BoFP replaces per-block patch clustering in FLIM networks with a single input-level clustering step that creates a bag of feature points used to define filters across all encoder blocks, yielding faster training for parasite detection in optical microscopy.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2512.01510","ref_index":59,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Semantic-aware Random Convolution and Source Matching for Domain Generalization in Medical Image Segmentation","primary_cat":"cs.CV","submitted_at":"2025-12-01T10:35:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Semantic-aware random convolution and intensity-based source matching enable effective single-source domain generalization for medical image segmentation, outperforming prior methods and sometimes matching in-domain performance.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2510.26635","ref_index":40,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"SAMRI: Segment Any MRI","primary_cat":"eess.IV","submitted_at":"2025-10-30T16:04:00+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SAMRI fine-tunes only the mask decoder of SAM on 1.1 million MRI slices from 30 datasets to reach mean DSC 0.87 on 47 targets and strong zero-shot performance.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2510.08052","ref_index":22,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"RASALoRE: Region Aware Spatial Attention with Location-based Random Embeddings for Weakly Supervised Anomaly Detection in Brain MRI Scans","primary_cat":"cs.CV","submitted_at":"2025-10-09T10:37:47+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A novel weakly supervised anomaly detection method for brain MRI that uses discriminative dual prompt tuning for pseudo masks and region-aware spatial attention with location-based random embeddings to achieve SOTA results with under 8 million parameters on BraTS and MSD datasets.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2501.13376","ref_index":18,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Clinical utility of foundation models in musculoskeletal MRI for biomarker fidelity and predictive outcomes","primary_cat":"eess.IV","submitted_at":"2025-01-23T04:41:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Fine-tuned foundation models produce reliable MSK MRI biomarkers that support workload-reducing triage and calibrated 48-month prediction of knee replacement and incident OA.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}