{"total":20,"items":[{"citing_arxiv_id":"2605.22572","ref_index":10,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"SegGuidedNet: Sub-Region-Aware Attention Supervision for Interpretable Brain Tumor Segmentation","primary_cat":"cs.CV","submitted_at":"2026-05-21T14:50:04+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"SegGuidedNet achieves Dice scores of 0.905 on BraTS2021 and 0.897 on BraTS2023 with sub-region attention supervision that adds under 0.2% parameters and provides free spatial interpretability.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.21835","ref_index":9,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation","primary_cat":"eess.IV","submitted_at":"2026-05-20T23:59:50+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A multi-center whole-body FDG PET/CT foundation model with early fusion and masked autoencoding pretraining achieves label-efficient tumor segmentation on downstream tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16208","ref_index":3,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"A Scalable Nonparametric Continuous-Time Survival Model through Numerical Quadrature","primary_cat":"stat.ML","submitted_at":"2026-05-15T17:25:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"QSurv uses Gauss-Legendre numerical quadrature and time-conditioned low-rank adaptation to enable scalable nonparametric continuous-time survival modeling with theoretical error bounds.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.14654","ref_index":22,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Beyond Instance-Level Self-Supervision in 3D Multi-Modal Medical Imaging","primary_cat":"cs.CV","submitted_at":"2026-05-14T10:10:34+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A self-supervised approach uses consistent spatial relationships of anatomical structures across patients to improve 3D multi-modal medical image representations, yielding modest gains on segmentation and classification tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.09025","ref_index":1,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"MedFL-Stress: A Systematic Robustness Evaluation of Federated Brain Tumor Segmentation under Cross-Hospital MRI Appearance Shift","primary_cat":"cs.CV","submitted_at":"2026-05-09T16:04:54+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"MedFL-Stress shows that FedBN reduces the best-to-worst hospital Dice gap by 41% versus FedAvg in federated brain tumor segmentation under simulated cross-hospital MRI shifts, improving the weakest site by 3.5 points with only a 0.5-point mean drop.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.07156","ref_index":5,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Hierarchical Perfusion Graphs for Tumor Heterogeneity Modeling in Glioma Molecular Subtyping","primary_cat":"cs.CV","submitted_at":"2026-05-08T02:42:38+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"HiPerfGNN uses VQ-VAE on DSC perfusion curves to form hierarchical tumor habitat graphs that predict IDH mutation (AUC 0.96 internal, 0.89 external), 1p/19q codeletion, and WHO grade.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"incorporated recurrent neural networks (RNNs) to directly process raw tempo- ral signals [10]. More recently, PerfGAT introduced graph attention networks for perfusion-based IDH prediction [29], but its reliance on predefined brain at- lases originally developed for healthy brains imposes rigid boundaries that fail to adapt to tumor-induced deformations [30,5]. These limitations motivate a shift from voxel-centric modeling toward entity-based representations. Graph Neural Networks (GNNs) naturally support this paradigm by encoding tumor subre- gions as nodes and their relationships as edges, enabling explicit modeling of intratumoral heterogeneity [23,18,29]. HiPerfGNN 3 Spatial-Temporal Cluster Mapping"},{"citing_arxiv_id":"2605.03432","ref_index":2,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"MK-ResRecon: Multi-Kernel Residual Framework for Texture-Aware 3D MRI Refinement from Sparse 2D Slices","primary_cat":"cs.CV","submitted_at":"2026-05-05T07:12:55+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"MK-ResRecon predicts missing slices with a multi-kernel texture-aware loss while IdentityRefineNet3D refines the combined 3D volume to enable accurate reconstruction from highly sparse 2D inputs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.02230","ref_index":3,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction","primary_cat":"cs.CV","submitted_at":"2026-05-04T05:02:51+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"InfiltrNet uses a CNN-Transformer hybrid with distance-transform labels to generate infiltration risk maps from MRI and outperforms baselines on BraTS 2020 and 2025 datasets.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.01563","ref_index":5,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Multi-Dataset Cross-Domain Knowledge Distillation for Unified Medical Image Segmentation, Classification, and Detection","primary_cat":"cs.CV","submitted_at":"2026-05-02T18:23:27+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A multi-dataset cross-domain knowledge distillation approach improves unified performance on medical image segmentation, classification, and detection by transferring domain-invariant features from a joint teacher model to task-specific students.","context_count":1,"top_context_role":"dataset","top_context_polarity":"use_dataset","context_text":"com/science/article/pii/S1361841523002608,doi:https://doi.org/10. 1016/j.media.2023.103000. [4] Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.S., Freymann, J.B., Farahani, K., Davatzikos, C., 2017. Advancing the cancer genome atlas glioma mri collections with expert segmen- tation labels and radiomic features. Scientific data 4, 1-13. [5] Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R.T., Berger, C., Ha, S.M., Rozycki, M., et al., 2018. Identifying the best machine learning algorithms for brain tumor segmentation,progressionassessment,andoverallsurvivalprediction in the brats challenge. arXiv preprint arXiv:1811.02629 . [6] Bilic, P., Christ, P., Li, H."},{"citing_arxiv_id":"2604.22177","ref_index":4,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Uni-Encoder Meets Multi-Encoders: Representation Before Fusion for Brain Tumor Segmentation with Missing Modalities","primary_cat":"cs.CV","submitted_at":"2026-04-24T03:02:26+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"UniME combines a pretrained unified ViT encoder with modality-specific CNN encoders to improve brain tumor segmentation performance when some MRI modalities are missing.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.13756","ref_index":7,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"MedRCube: A Multidimensional Framework for Fine-Grained and In-Depth Evaluation of MLLMs in Medical Imaging","primary_cat":"cs.CL","submitted_at":"2026-04-15T11:41:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"MedRCube is a new fine-grained evaluation framework that benchmarks 33 MLLMs on medical imaging, ranks Lingshu-32B highest, and finds a significant positive link between shortcut behaviors and diagnostic performance.","context_count":1,"top_context_role":"dataset","top_context_polarity":"use_dataset","context_text":"Carlos Martín-Isla, Víctor M Campello, Cristian Izquierdo, Kaisar Kushibar, Carla Sendra-Balcells, Polyxeni Gkontra, Alireza Sojoudi, Mitchell J Ful- ton, Tewodros Weldebirhan Arega, Kumaradevan Punithakumar, and 1 others. 2023. Deep learning segmentation of the right ventricle in cardiac mri: the m&ms challenge.IEEE Journal of Biomedical and Health Informatics, 27(7):3302-3313. Bjoern H Menze, Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Keyvan Farahani, Justin Kirby, Yuliya Burren, Nicole Porz, Johannes Slotboom, Roland Wiest, and 1 others. 2014. The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging, 34(10):1993- 2024. National Board of Medical Examiners."},{"citing_arxiv_id":"2604.08893","ref_index":39,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Adaptive Dual Residual U-Net with Attention Gate and Multiscale Spatial Attention Mechanisms (ADRUwAMS)","primary_cat":"cs.CV","submitted_at":"2026-04-10T02:57:39+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"ADRUwAMS reports Dice scores of 0.9229 (whole tumor), 0.8432 (tumor core), and 0.8004 (enhancing tumor) on BraTS 2020 after training on BraTS 2019/2020 datasets.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.05651","ref_index":11,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Probing Intrinsic Medical Task Relationships: A Contrastive Learning Perspective","primary_cat":"cs.CV","submitted_at":"2026-04-07T09:54:37+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"TaCo contrastively embeds semantic, generative, and transformation tasks from medical imaging into a joint space to reveal which tasks cluster, blend, or remain distinct.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2603.29977","ref_index":3,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Quantifying Cross-Modal Interactions in Multimodal Glioma Survival Prediction via InterSHAP: Evidence for Additive Signal Integration","primary_cat":"cs.LG","submitted_at":"2026-03-31T16:39:56+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Multimodal glioma survival models achieve better performance through additive integration of image and RNA features rather than cross-modal synergies, as quantified by lower measured interactions in stronger architectures.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.09656","ref_index":6,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Fairboard: a quantitative framework for equity assessment of healthcare models","primary_cat":"cs.LG","submitted_at":"2026-03-30T11:02:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Patient identity and clinical features predict brain tumor segmentation accuracy more strongly than model choice, with localized spatial biases consistent across models and no formal fairness guarantees in any.","context_count":1,"top_context_role":"dataset","top_context_polarity":"use_dataset","context_text":"2014.2377694. [5] Spyridon Bakas, Hamed Akbari, Aristeidis Sotiras, Michel Bilello, Martin Rozycki, Justin S. Kirby, John B. Freymann, Keyvan Farahani, and Christos Davatzikos. Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features.Scientific Data, 4: 170117, 2017. doi:10.1038/sdata.2017.117. [6] Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell T. Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, et al. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in a multi-institutional multi-site dataset."},{"citing_arxiv_id":"2511.17146","ref_index":25,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Learning to Look Closer: A New Instance-Wise Loss for Small Cerebral Lesion Segmentation","primary_cat":"cs.CV","submitted_at":"2025-11-21T11:10:05+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"CC-DiceCE boosts recall for small lesion segmentation in MRI with minimal degradation in other metrics and generally outperforms blob loss across datasets.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2510.26635","ref_index":56,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"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":5,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"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":"2502.11638","ref_index":30,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Safeguarding AI in Medical Imaging: Post-Hoc Out-of-Distribution Detection with Normalizing Flows","primary_cat":"cs.CV","submitted_at":"2025-02-17T10:31:24+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Post-hoc normalizing flows for OOD detection in medical imaging achieve 84.61% AUROC on MedOOD and 93.8% on MedMNIST, outperforming ViM, MDS, and ReAct.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2107.02314","ref_index":13,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification","primary_cat":"cs.CV","submitted_at":"2021-07-05T23:12:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"BraTS 2021 provides a 2,040-patient mpMRI benchmark for brain tumor sub-region segmentation and MGMT methylation classification, hosted on Synapse and Kaggle with $60,000 in awards.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}