MICCAI
Collection
Accepted papers for MICCAI (Medical Image Computing and Computer Assisted Intervention), one dataset per year. • 5 items • Updated
paper_id stringlengths 9 9 | title stringlengths 33 163 | authors listlengths 1 23 | miccai_url stringlengths 57 57 | pdf_url stringlengths 58 58 | doi stringclasses 0
values | sharedit_url stringclasses 0
values | supplementary_url stringclasses 62
values | topics listlengths 2 11 | code_urls listlengths 0 2 | dataset_urls listlengths 0 12 | pages stringclasses 0
values | bibtex large_stringlengths 416 954 | abstract large_stringlengths 658 2.09k ⌀ | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Paper2705 | 3D Cerebrovascular Shape Completion from Biplane Angiography and CTA Prior | [
"Janik Jehkul",
"Sarah Frisken",
"Vivek Gopalakrishnan",
"Daniel Rueckert",
"Nazim Haouchine"
] | https://papers.miccai.org/miccai-2026/0001-Paper2705.html | https://papers.miccai.org/miccai-2026/paper/2705_paper.pdf | null | null | https://papers.miccai.org/miccai-2026/supp/2705_supp.zip | [
"Body -> Vasculature",
"Modalities -> CT / X-ray",
"Applications -> Image-Guided Interventions",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/janik-j/cta-dsa-fusion"
] | [
"https://topbrain2025.grand-challenge.org/data/"
] | null | @InProceedings{JehJan_3D_MICCAI2026,
author = { Jehkul, Janik AND Frisken, Sarah AND Gopalakrishnan, Vivek AND Rueckert, Daniel AND Haouchine, Nazim},
title = { { 3D Cerebrovascular Shape Completion from Biplane Angiography and CTA Prior } },
booktitle = {Medical Image Computing and Computer Ass... | We propose a novel approach to bridge the resolution and dimensionality gap between CTA, which provides 3D vascular geometry but often misses small vessels, and biplanar 2D DSA, which offers higher spatial resolution but lacks 3D structural information, by formulating the problem as a 3D shape completion task. Our meth... | null | null |
Paper5533 | 3D Classification of Paramagnetic Rim Lesions in Multiple Sclerosis via Asymmetric QSM–FLAIR Modeling | [
"Veronica Pignedoli",
"Giacomo Boffa",
"Nicoletta Noceti",
"Matilde Inglese",
"Francesca Odone",
"Matteo Moro"
] | https://papers.miccai.org/miccai-2026/0002-Paper5533.html | https://papers.miccai.org/miccai-2026/paper/5533_paper.pdf | null | null | null | [
"Body -> Brain",
"Modalities -> MRI",
"Applications -> Anomaly / Lesion Detection",
"Applications -> Computer-Aided Diagnosis",
"Machine Learning -> Deep Learning",
"Machine Learning -> Multimodal Models / LLMs / VLMs"
] | [
"https://github.com/veronicapignedoli/FRODO"
] | [] | null | @InProceedings{PigVer_3D_MICCAI2026,
author = { Pignedoli, Veronica AND Boffa, Giacomo AND Noceti, Nicoletta AND Inglese, Matilde AND Odone, Francesca AND Moro, Matteo},
title = { { 3D Classification of Paramagnetic Rim Lesions in Multiple Sclerosis via Asymmetric QSM–FLAIR Modeling } },
booktit... | Paramagnetic rim lesions (Rim+) identified on susceptibility-sensitive MRI have recently emerged as a specific biomarker of chronic active inflammation in Multiple Sclerosis (MS) and are associated with long-term disability progression. However, susceptibility imaging and expert interpretation remain limited to special... | 2606.16756 | title_snapshot |
Paper4553 | A 3D Unrolling Framework for Joint Sodium MRI Reconstruction and Concentration Quantification | [
"Yilin Cao",
"Caohui Duan",
"Dinggang Shen",
"Xin Lou",
"Kaicong Sun"
] | https://papers.miccai.org/miccai-2026/0003-Paper4553.html | https://papers.miccai.org/miccai-2026/paper/4553_paper.pdf | null | null | null | [
"Body -> Brain",
"Modalities -> MRI",
"Applications -> Image Reconstruction",
"Machine Learning -> Deep Learning"
] | [] | [] | null | @InProceedings{CaoYil_A3D_MICCAI2026,
author = { Cao, Yilin AND Duan, Caohui AND Shen, Dinggang AND Lou, Xin AND Sun, Kaicong},
title = { { A 3D Unrolling Framework for Joint Sodium MRI Reconstruction and Concentration Quantification } },
booktitle = {Medical Image Computing and Computer Assiste... | Sodium MRI can non-invasively measure tissue sodium concentration (TSC), a key biomarker for stroke, tumor, cartilage degeneration, and other diseases, but severely suffers from intrinsically low signal-to-noise ratio and long scan times. Existing methods typically perform denoising on conventionally reconstructed imag... | null | null |
Paper2852 | A Clinical Guideline-Grounded Hybrid Agentic Framework for Holistic Epilepsy Management | [
"Duy Khoa Pham",
"Dinesh Giritharan",
"Guilherme Camargo de Oliveira",
"Bao Quoc Vo",
"Karin Verspoor",
"Meng Law",
"Patrick Kwan",
"Zongyuan Ge",
"Deval Mehta"
] | https://papers.miccai.org/miccai-2026/0004-Paper2852.html | https://papers.miccai.org/miccai-2026/paper/2852_paper.pdf | null | null | null | [
"Body -> Brain",
"Modalities -> EEG / MEG / ECG / Physiological Signals",
"Modalities -> MRI",
"Applications -> Computer-Aided Diagnosis",
"Applications -> Multimodal Integration with Clinical / Genomic / Biomarkers",
"Applications -> Outcome Prediction / Prognosis / Longitudinal Modeling",
"Machine Lea... | [
"https://github.com/khoapham154/epi_guide"
] | [] | null | @InProceedings{PhaDuy_AClinical_MICCAI2026,
author = { Pham, Duy Khoa AND Giritharan, Dinesh AND Camargo de Oliveira, Guilherme AND Vo, Bao Quoc AND Verspoor, Karin AND Law, Meng AND Kwan, Patrick AND Ge, Zongyuan AND Mehta, Deval},
title = { { A Clinical Guideline-Grounded Hybrid Agentic Framework for ... | Epilepsy is a chronic neurological disorder requiring multi-faceted management, including seizure detection, syndrome diagnosis, prognostication, antiseizure medication recommendation, epileptogenic zone localization, and surgical outcome prediction. Although numerous deep learning approaches have been developed for in... | null | null |
Paper1600 | A Confounder-aware Representation Learning Framework for Alzheimer’s Disease Classification | [
"Yuanhao Chen",
"Jingwen Chen",
"Jinyi Xu",
"Haoqi Yu",
"Bin Wang",
"Chunzhong Li",
"Yongquan Zhang",
"Fenglei Fan",
"Ahmed Elazab",
"Xiang Wan",
"Changmiao Wang"
] | https://papers.miccai.org/miccai-2026/0005-Paper1600.html | https://papers.miccai.org/miccai-2026/paper/1600_paper.pdf | null | null | null | [
"Body -> Brain",
"Modalities -> MRI",
"Applications -> Computer-Aided Diagnosis",
"Machine Learning -> Deep Learning",
"Machine Learning -> Multimodal Models / LLMs / VLMs"
] | [
"https://github.com/redtea-code/Causal_fusion"
] | [] | null | @InProceedings{CheYua_AConfounderaware_MICCAI2026,
author = { Chen, Yuanhao AND Chen, Jingwen AND Xu, Jinyi AND Yu, Haoqi AND Wang, Bin AND Li, Chunzhong AND Zhang, Yongquan AND Fan, Fenglei AND Elazab, Ahmed AND Wan, Xiang AND Wang, Changmiao},
title = { { A Confounder-aware Representation Learning Fra... | Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder, and current treatments cannot reverse its progression, making early diagnosis and intervention essential. Deep learning methods have shown promise for diagnosing AD from neuroimaging data, but these models are often affected by both visible and ... | null | null |
Paper6105 | A Counterfactual Framework for Directional Cell–Cell Interaction Analysis in Spatial Transcriptomics | [
"Humaira Anzum",
"Veena Kochat",
"Md Ishtyaq Mahmud",
"Jagan Mohan Reddy Dwarampudi",
"Suresh Satpati",
"Pooja Shukla",
"Milind Javle",
"Lawrence Kwong",
"Kunal Rai",
"Tania Banerjee"
] | https://papers.miccai.org/miccai-2026/0006-Paper6105.html | https://papers.miccai.org/miccai-2026/paper/6105_paper.pdf | null | null | null | [
"Body -> Abdomen",
"Modalities -> Microscopy",
"Applications -> Computational / Integrative Pathology",
"Applications -> Connectivity & Network Analysis",
"Machine Learning -> Causal Inference / Counterfactual Reasoning",
"Machine Learning -> Deep Learning",
"Machine Learning -> Evaluation / Benchmarkin... | [
"https://github.com/Snigdha022/Counterfactual-Cell-Influence-Spatial-Transcriptomics.git"
] | [
"https://github.com/Snigdha022/Counterfactual-Cell-Influence-Spatial-Transcriptomics/tree/main/data/raw"
] | null | @InProceedings{AnzHum_ACounterfactual_MICCAI2026,
author = { Anzum, Humaira AND Kochat, Veena AND Mahmud, Md Ishtyaq AND Dwarampudi, Jagan Mohan Reddy AND Satpati, Suresh AND Shukla, Pooja AND Javle, Milind AND Kwong, Lawrence AND Rai, Kunal AND Banerjee, Tania},
title = { { A Counterfactual Framework f... | Understanding neighboring cell influences is central to spatial transcriptomics, yet existing methods rely on correlation or predefined ligand–receptor (LR) pairs and do not test directionality. We introduce a counterfactual, intervention-based framework for inferring directional cell–cell influence that is LR-agnostic... | null | null |
Paper4859 | A Deep Learning Surrogate Model for Microwave Thermal Ablation: From Preoperative Planning to Intraoperative Re-planning | [
"Ilias Nahmed",
"Francesco Dettori",
"Michel Duprez",
"Pablo Alvarez",
"Stéphane Cotin"
] | https://papers.miccai.org/miccai-2026/0007-Paper4859.html | https://papers.miccai.org/miccai-2026/paper/4859_paper.pdf | null | null | null | [
"Body -> Abdomen",
"Modalities -> CT / X-ray",
"Applications -> Digital Twins / Simulation / Synthetic Data",
"Applications -> Outcome Prediction / Prognosis / Longitudinal Modeling",
"Machine Learning -> Deep Learning",
"Machine Learning -> Synthetic Data / Data-centric AI",
"Surgery -> Planning & Simu... | [] | [
"https://www.ircad.fr/research/data-sets/liver-segmentation-3d-ircadb-01/"
] | null | @InProceedings{NahIli_ADeep_MICCAI2026,
author = { Nahmed, Ilias AND Dettori, Francesco AND Duprez, Michel AND Alvarez, Pablo AND Cotin, Stéphane},
title = { { A Deep Learning Surrogate Model for Microwave Thermal Ablation: From Preoperative Planning to Intraoperative Re-planning } },
booktitle ... | With the increasing interest in thermal ablation therapies, patient-specific predictive simulations have become crucial for improving both preoperative planning and intraoperative corrections. Planning strategies typically rely on the solution of an inverse problem, yet high-fidelity finite element simulations remain c... | null | null |
Paper4457 | A Flexible Structure-Guided Feature Aggregation Paradigm for Efficient Vascular Representation | [
"Yaolei Qi",
"Wenbo Peng",
"Tong Wang",
"Chenwei Xu",
"Yuan Zhang",
"Guanyu Yang"
] | https://papers.miccai.org/miccai-2026/0008-Paper4457.html | https://papers.miccai.org/miccai-2026/paper/4457_paper.pdf | null | null | null | [
"Body -> Vasculature",
"Modalities -> CT / X-ray",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/YaoleiQi/VSP"
] | [] | null | @InProceedings{QiYao_AFlexible_MICCAI2026,
author = { Qi, Yaolei AND Peng, Wenbo AND Wang, Tong AND Xu, Chenwei AND Zhang, Yuan AND Yang, Guanyu},
title = { { A Flexible Structure-Guided Feature Aggregation Paradigm for Efficient Vascular Representation } },
booktitle = {Medical Image Computing ... | Accurate representation of vascular structures is fundamental for a wide range of clinical applications and remains challenging due to the slender geometry, complex branching topology, and long-range continuity inherent to tree-like vasculature. Although structural priors have been widely explored in vascular image ana... | null | null |
Paper5049 | A Guideline-Aware AI Agent for Zero-Shot Target Volume Auto-Delineation | [
"Yoon Jo Kim",
"Wonyoung Cho",
"Jongmin Lee",
"Han Joo Chae",
"Hyunki Park",
"Sang Hoon Seo",
"Jae Myung Noh",
"Kyungmi Yang",
"Dongryul Oh",
"Jin Sung Kim"
] | https://papers.miccai.org/miccai-2026/0009-Paper5049.html | https://papers.miccai.org/miccai-2026/paper/5049_paper.pdf | null | null | null | [
"Body -> Lung / Thoracic",
"Modalities -> CT / X-ray",
"Applications -> Image Segmentation",
"Machine Learning -> Multimodal Models / LLMs / VLMs"
] | [
"https://github.com/Oncosoft-Research/OncoAgent"
] | [] | null | @InProceedings{KimYoo_AGuidelineAware_MICCAI2026,
author = { Kim, Yoon Jo AND Cho, Wonyoung AND Lee, Jongmin AND Chae, Han Joo AND Park, Hyunki AND Seo, Sang Hoon AND Noh, Jae Myung AND Yang, Kyungmi AND Oh, Dongryul AND Kim, Jin Sung},
title = { { A Guideline-Aware AI Agent for Zero-Shot Target Volume ... | Delineating the clinical target volume (CTV) in radiotherapy involves complex margins constrained by tumor location and anatomical barriers. While deep learning models automate this process, their rigid reliance on expert-annotated data requires costly retraining whenever clinical guidelines update. To overcome this li... | 2603.09448 | title_snapshot |
Paper1042 | A Heterogeneous Prognosis Prediction Framework with Global Brain Connectivity and Local Image Features | [
"Jingfeng Lin",
"Junjun Pan",
"Jinda Wang",
"Zhenyu Tang"
] | https://papers.miccai.org/miccai-2026/0010-Paper1042.html | https://papers.miccai.org/miccai-2026/paper/1042_paper.pdf | null | null | null | [
"Body -> Brain",
"Modalities -> CT / X-ray",
"Modalities -> Diffusion MRI",
"Modalities -> Functional MRI",
"Applications -> Outcome Prediction / Prognosis / Longitudinal Modeling",
"Machine Learning -> Deep Learning"
] | [] | [
"https://cam-can.mrc-cbu.cam.ac.uk/dataset/",
"https://www.cancerimagingarchive.net/collection/ucsf-pdgm/"
] | null | @InProceedings{LinJin_AHeterogeneous_MICCAI2026,
author = { Lin, Jingfeng AND Pan, Junjun AND Wang, Jinda AND Tang, Zhenyu},
title = { { A Heterogeneous Prognosis Prediction Framework with Global Brain Connectivity and Local Image Features } },
booktitle = {Medical Image Computing and Computer A... | Diffuse glioma is the most prevalent malignant brain tumor, and accurate prognosis prediction is essential in personalized treatment to improve outcomes. Although numerous deep learning based methods have been proposed for prognosis prediction, most of them rely solely on local image features of tumor regions. They neg... | null | null |
Paper0766 | A Hierarchical Multi-Task Framework for Dementia Diagnosis via Pathological Feature Learning from Multi-Organ Data | [
"Shilun Zhao",
"Fan Li",
"Zhichao Liang",
"Kaicong Sun",
"Shuwei Bai",
"Weilin Zhou",
"Xin Lin",
"Zihao Wang",
"Dinggang Shen"
] | https://papers.miccai.org/miccai-2026/0011-Paper0766.html | https://papers.miccai.org/miccai-2026/paper/0766_paper.pdf | null | null | null | [
"Body -> Brain",
"Body -> Whole-body / Multi-organ / Other",
"Modalities -> Multimodal Sensor Fusion",
"Applications -> Computer-Aided Diagnosis",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/AIbySlz/MoMmNet"
] | [] | null | @InProceedings{ZhaShi_AHierarchical_MICCAI2026,
author = { Zhao, Shilun AND Li, Fan AND Liang, Zhichao AND Sun, Kaicong AND Bai, Shuwei AND Zhou, Weilin AND Lin, Xin AND Wang, Zihao AND Shen, Dinggang},
title = { { A Hierarchical Multi-Task Framework for Dementia Diagnosis via Pathological Feature Learn... | Current deep learning methods for dementia diagnosis are predominantly brain-centric, neglecting the disease’s systemic nature. Furthermore, these models fail to decouple disease-specific alterations from normal aging, and often limit diagnosis to a single etiology, neglecting the reality of co-occurring conditions. To... | null | null |
Paper1806 | A Modality-Aware Mixture-of-Experts Framework for Clinical Multimodal Breast Ultrasound Diagnosis | [
"Hao Suo",
"Zhongjun Zhu",
"Lingyu Chen",
"Qing Chang",
"Hongen Liao",
"Lin Jin",
"Cheng Li",
"Fang Chen"
] | https://papers.miccai.org/miccai-2026/0012-Paper1806.html | https://papers.miccai.org/miccai-2026/paper/1806_paper.pdf | null | null | null | [
"Body -> Breast",
"Modalities -> Ultrasound",
"Applications -> Multimodal Integration with Clinical / Genomic / Biomarkers",
"Surgery -> Data Science in Surgery",
"Special Topic -> Clinical Trials & Deployment"
] | [
"https://github.com/StveHok/MA-MoE"
] | [] | null | @InProceedings{SuoHao_AModalityAware_MICCAI2026,
author = { Suo, Hao AND Zhu, Zhongjun AND Chen, Lingyu AND Chang, Qing AND Liao, Hongen AND Jin, Lin AND Li, Cheng AND Chen, Fang},
title = { { A Modality-Aware Mixture-of-Experts Framework for Clinical Multimodal Breast Ultrasound Diagnosis } },
... | Current breast ultrasound CADx models are often insufficiently validated under real-world clinical heterogeneity, including spatial variability, long-tailed phenotypes, and multi-vendor acquisition variability. To bridge this gap, we construct a comprehensive multimodal benchmark capturing these complexities across pai... | null | null |
Paper5454 | A Multi-center Benchmark for Abdominal Disease Diagnosis and Report Generation from Non-Contrast CT | [
"Mariam Elbakry",
"Aliaa Sayed Sheha",
"Salma Hassan Tantawy",
"Aya Yassin",
"Concetto Spampinato",
"Karim Lekadir",
"Xiaomeng Li",
"Marawan Elbatel"
] | https://papers.miccai.org/miccai-2026/0013-Paper5454.html | https://papers.miccai.org/miccai-2026/paper/5454_paper.pdf | null | null | null | [
"Body -> Abdomen",
"Body -> Pelvis / Reproductive System",
"Body -> Whole-body / Multi-organ / Other",
"Modalities -> CT / X-ray",
"Applications -> Anomaly / Lesion Detection",
"Special Topic -> AI for Neglected Diseases & Limited-Resource Settings",
"Special Topic -> AI for Population Health, Public He... | [
"https://github.com/xmed-lab/TriALS-Report/"
] | [] | null | @InProceedings{ElbMar_AMulticenter_MICCAI2026,
author = { Elbakry, Mariam AND Sheha, Aliaa Sayed AND Tantawy, Salma Hassan AND Yassin, Aya AND Spampinato, Concetto AND Lekadir, Karim AND Li, Xiaomeng AND Elbatel, Marawan},
title = { { A Multi-center Benchmark for Abdominal Disease Diagnosis and Report G... | Multiphasic contrast-enhanced CT (CECT) is widely used for abdominal lesion characterization, yet it carries inherent risks of contrast-induced nephropathy, escalates acquisition burden, and heavily contributes to radiologist workload. To address these challenges, we introduce a novel multi-center benchmark for multi-o... | 2606.16991 | title_snapshot |
Paper4243 | A Multimodal 3D Foundation Model for Light Sheet Fluorescence Microscopy Enables Few-Shot Segmentation, Classification, and Deblurring | [
"Adina Scheinfeld",
"Haotan Zhang",
"Shang Mu",
"Rudolf L. M. van Herten",
"Lucas Stoffl",
"Ali Ertürk",
"Zhuhao Wu",
"Johannes C. Paetzold"
] | https://papers.miccai.org/miccai-2026/0014-Paper4243.html | https://papers.miccai.org/miccai-2026/paper/4243_paper.pdf | null | null | null | [
"Body -> Brain",
"Modalities -> Microscopy",
"Applications -> Image Segmentation",
"Applications -> Other",
"Machine Learning -> Deep Learning",
"Machine Learning -> Foundation Models",
"Machine Learning -> Multimodal Models / LLMs / VLMs"
] | [
"https://github.com/AdinaScheinfeld/lsm_fm_public_repo.git"
] | [
"https://zenodo.org/records/20149070"
] | null | @InProceedings{SchAdi_AMultimodal_MICCAI2026,
author = { Scheinfeld, Adina AND Zhang, Haotan AND Mu, Shang AND van Herten, Rudolf L. M. AND Stoffl, Lucas AND Ertürk, Ali AND Wu, Zhuhao AND Paetzold, Johannes C.},
title = { { A Multimodal 3D Foundation Model for Light Sheet Fluorescence Microscopy Enable... | Light sheet fluorescence microscopy (LSM) enables high-resolution, three-dimensional (3D) imaging of biological specimens, providing rich volumetric data for studying cellular organization, pathology, and vascular networks. However, the size, dimensionality, and annotation burden of LSM data make supervised deep learni... | 2605.26026 | title_snapshot |
Paper0825 | A Multi-view, Hybrid, Hypergraph Learning Framework with Causal Perturbation for Early Alzheimer’s Disease Diagnosis | [
"Yifan Jia",
"Luoyu Wang",
"Yitian Tao",
"Zihao Zhu",
"Yawen Zhang",
"Han Zhang"
] | https://papers.miccai.org/miccai-2026/0015-Paper0825.html | https://papers.miccai.org/miccai-2026/paper/0825_paper.pdf | null | null | null | [
"Body -> Brain",
"Modalities -> Functional MRI",
"Applications -> Computer-Aided Diagnosis",
"Applications -> Connectivity & Network Analysis",
"Applications -> Neuropsychiatry & Mental Health Imaging",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/eyeopeners/HyperBrainNet"
] | [
"https://adni.loni.usc.edu/"
] | null | @InProceedings{JiaYif_AMultiview_MICCAI2026,
author = { Jia, Yifan AND Wang, Luoyu AND Tao, Yitian AND Zhu, Zihao AND Zhang, Yawen AND Zhang, Han},
title = { { A Multi-view, Hybrid, Hypergraph Learning Framework with Causal Perturbation for Early Alzheimer’s Disease Diagnosis } },
booktitle = {M... | Early diagnosis of Alzheimer’s disease (AD) is paramount for effective intervention. Resting-state functional magnetic resonance imaging (rs-fMRI) offers critical insights into how brain connectivity networks transition from cognitively normal (CN) to mild cognitive impairment (MCI, the early stage of AD) status. Most ... | null | null |
Paper2068 | A Neurosymbolic Framework for Interpretable Skeleton-Based Seizure Detection via Concept-Driven Logical Reasoning | [
"Talha Ilyas",
"Deval Mehta",
"Zongyuan Ge"
] | https://papers.miccai.org/miccai-2026/0016-Paper2068.html | https://papers.miccai.org/miccai-2026/paper/2068_paper.pdf | null | null | https://papers.miccai.org/miccai-2026/supp/2068_supp.zip | [
"Body -> Brain",
"Modalities -> Other",
"Modalities -> Photograph / Video",
"Applications -> Computer-Aided Diagnosis",
"Applications -> Image-Guided Interventions",
"Machine Learning -> Deep Learning",
"Machine Learning -> Interpretability / Explainability"
] | [
"https://github.com/Mr-TalhaIlyas/CDSD"
] | [
"https://github.com/Mr-TalhaIlyas/CDSD"
] | null | @InProceedings{IlyTal_ANeurosymbolic_MICCAI2026,
author = { Ilyas, Talha AND Mehta, Deval AND Ge, Zongyuan},
title = { { A Neurosymbolic Framework for Interpretable Skeleton-Based Seizure Detection via Concept-Driven Logical Reasoning } },
booktitle = {Medical Image Computing and Computer Assist... | Video-based seizure detection is essential for the management of epilepsy patients, offering a non-invasive complement to electroencephalography. While several deep learning approaches have been developed for video-based seizure detection, none are inherently interpretable, limiting their adoption and translation into ... | 2606.21252 | title_snapshot |
Paper6055 | A Real-World Evaluation of Failure Detection for Liver CT Segmentation | [
"Jeddy Bennett",
"McKell Woodland",
"Austin Castelo",
"Mais Altaie",
"Ajith Anthony",
"Noreen S. Siddiqi",
"James P. Long",
"Kristy K. Brock"
] | https://papers.miccai.org/miccai-2026/0017-Paper6055.html | https://papers.miccai.org/miccai-2026/paper/6055_paper.pdf | null | null | null | [
"Body -> Abdomen",
"Modalities -> CT / X-ray",
"Applications -> Image Segmentation",
"Machine Learning -> Uncertainty Quantification"
] | [
"https://github.com/mckellwoodland/liver_ct_ood_translation"
] | [] | null | @InProceedings{BenJed_ARealWorld_MICCAI2026,
author = { Bennett, Jeddy AND Woodland, McKell AND Castelo, Austin AND Altaie, Mais AND Anthony, Ajith AND Siddiqi, Noreen S. AND Long, James P. AND Brock, Kristy K.},
title = { { A Real-World Evaluation of Failure Detection for Liver CT Segmentation } },
... | Deep learning models deployed in clinical imaging frequently encounter distribution shifts, yet most out‑of‑distribution (OOD) detection methods are evaluated only on controlled research datasets. As a result, it is unclear whether existing approaches can reliably identify segmentation failures that arise in real‑world... | null | null |
Paper3573 | A Unified Few-Shot Framework for Multi-Atlas Neuroimaging Segmentation Leveraging Vision Foundation Models | [
"Bocheng Guo",
"Xiyuan Zhang",
"Wei Zhang",
"Ofer Pasternak",
"Lauren J. O’Donnell",
"Fan Zhang"
] | https://papers.miccai.org/miccai-2026/0018-Paper3573.html | https://papers.miccai.org/miccai-2026/paper/3573_paper.pdf | null | null | null | [
"Body -> Brain",
"Modalities -> MRI",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning",
"Machine Learning -> Foundation Models"
] | [
"https://github.com/GBCWORLDWALKER/FewshotBrain"
] | [
"https://www.humanconnectomeproject.org/data/"
] | null | @InProceedings{GuoBoc_AUnified_MICCAI2026,
author = { Guo, Bocheng AND Zhang, Xiyuan AND Zhang, Wei AND Pasternak, Ofer AND O’Donnell, Lauren J. AND Zhang, Fan},
title = { { A Unified Few-Shot Framework for Multi-Atlas Neuroimaging Segmentation Leveraging Vision Foundation Models } },
booktitle ... | Accurate brain segmentation across diverse atlas protocols is essential for neuroimaging, yet it is hindered by the high cost of generating large-scale ground truth labels and the prohibitive overhead of re-training models for new protocols. While vision foundation models offer powerful representations, their seamless ... | null | null |
Paper5165 | A Unified Framework for Joint Detection of Lacunes and Enlarged Perivascular Spaces | [
"Lucas He",
"Krinos Li",
"Hanyuan Zhang",
"Runlong He",
"Silvia Ingala",
"Luigi Lorenzini",
"Marleen de Bruijne",
"Frederik Barkhof",
"Rhodri Davies",
"Carole H. Sudre"
] | https://papers.miccai.org/miccai-2026/0019-Paper5165.html | https://papers.miccai.org/miccai-2026/paper/5165_paper.pdf | null | null | null | [
"Body -> Brain",
"Modalities -> MRI",
"Applications -> Anomaly / Lesion Detection",
"Machine Learning -> Deep Learning",
"Machine Learning -> Semi- / Weakly- / Self-supervised Learning"
] | [
"https://github.com/lucas-ucl/Lacune-EPVS-MTL"
] | [
"https://zenodo.org/records/4520773",
"https://ep-ad.org"
] | null | @InProceedings{HeLuc_AUnified_MICCAI2026,
author = { He, Lucas AND Li, Krinos AND Zhang, Hanyuan AND He, Runlong AND Ingala, Silvia AND Lorenzini, Luigi AND de Bruijne, Marleen AND Barkhof, Frederik AND Davies, Rhodri AND Sudre, Carole H.},
title = { { A Unified Framework for Joint Detection of Lacunes ... | Cerebral small vessel disease (CSVD) markers, specifically enlarged perivascular spaces (EPVS) and lacunae, present a unique challenge in medical image analysis due to their radiological mimicry. Standard segmentation networks struggle with feature interference and extreme class imbalance when handling these divergent ... | 2603.04243 | title_snapshot |
Paper1637 | A Versatile Prompt-Driven Framework for Brain Segmentation, Parcellation, and Surface Reconstruction from Diverse Modalities | [
"Zifeng Lian",
"Jiameng Liu",
"Shui Cao",
"Zhichao Liang",
"Feng Shi",
"Dinggang Shen"
] | https://papers.miccai.org/miccai-2026/0020-Paper1637.html | https://papers.miccai.org/miccai-2026/paper/1637_paper.pdf | null | null | null | [
"Body -> Brain",
"Modalities -> MRI",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning"
] | [] | [] | null | @InProceedings{LiaZif_AVersatile_MICCAI2026,
author = { Lian, Zifeng AND Liu, Jiameng AND Cao, Shui AND Liang, Zhichao AND Shi, Feng AND Shen, Dinggang},
title = { { A Versatile Prompt-Driven Framework for Brain Segmentation, Parcellation, and Surface Reconstruction from Diverse Modalities } },
... | Accurate and comprehensive characterization of brain anatomy across volumetric and surface domains is fundamental to neuroimaging research and clinical practice. Existing methods, however, are often modality-specific and task-restricted, requiring separate models for segmentation, parcellation, or surface reconstructio... | null | null |
Paper5543 | AbdomenGen: Sequential Volume-Conditioned Diffusion Framework for Abdominal Anatomy Generation | [
"Yubraj Bhandari",
"Lavsen Dahal",
"Paul Segars",
"Joseph Y. Lo"
] | https://papers.miccai.org/miccai-2026/0021-Paper5543.html | https://papers.miccai.org/miccai-2026/paper/5543_paper.pdf | null | null | null | [
"Body -> Abdomen",
"Modalities -> CT / X-ray",
"Applications -> Digital Twins / Simulation / Synthetic Data",
"Machine Learning -> Deep Learning"
] | [] | [] | null | @InProceedings{BhaYub_AbdomenGen_MICCAI2026,
author = { Bhandari, Yubraj AND Dahal, Lavsen AND Segars, Paul AND Lo, Joseph Y.},
title = { { AbdomenGen: Sequential Volume-Conditioned Diffusion Framework for Abdominal Anatomy Generation } },
booktitle = {Medical Image Computing and Computer Assist... | Computational phantoms are widely used in medical imaging research, yet current systems to generate controlled, clinically meaningful anatomical variations remain limited. We present AbdomenGen, a sequential volume-conditioned diffusion framework for controllable abdominal anatomy generation. We introduce the Volume Co... | 2604.12969 | title_snapshot |
Paper4515 | ACA: Post-hoc Adaptive Logit Alignment for 3D Medical Image Segmentation | [
"Nanyu Dong",
"Qi Chen",
"Johan Verjans",
"Zhibin Liao"
] | https://papers.miccai.org/miccai-2026/0022-Paper4515.html | https://papers.miccai.org/miccai-2026/paper/4515_paper.pdf | null | null | null | [
"Body -> Whole-body / Multi-organ / Other",
"Modalities -> CT / X-ray",
"Modalities -> MRI",
"Applications -> Image Segmentation",
"Machine Learning -> Fairness / Safety / Reliability"
] | [
"https://github.com/MelindaDong/aca-segmentation"
] | [] | null | @InProceedings{DonNan_ACA_MICCAI2026,
author = { Dong, Nanyu AND Chen, Qi AND Verjans, Johan AND Liao, Zhibin},
title = { { ACA: Post-hoc Adaptive Logit Alignment for 3D Medical Image Segmentation } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026},
... | Despite the rapid advancement of 3D medical image segmentation foundation models trained on large-scale datasets, generalization to unseen data remains challenging. We observe that performance degradation is closely associated with localized voxel-level confidence distribution shifts between training and testing data. ... | null | null |
Paper4622 | Accurate Reconstruction of the Standard 12-Lead ECG from a Single Lead Based on Inherent Principles of ECG | [
"Dong-hyuk Seo",
"Ui Jong Kim",
"Yong-Yeon Jo",
"Junmyung Kwon",
"Won-Yong Shin",
"Sang-Wook Kim"
] | https://papers.miccai.org/miccai-2026/0023-Paper4622.html | https://papers.miccai.org/miccai-2026/paper/4622_paper.pdf | null | null | null | [
"Body -> Cardiac",
"Modalities -> EEG / MEG / ECG / Physiological Signals",
"Modalities -> Mobile / Wearable / Point-of-Care Imaging & Sensors",
"Applications -> Computer-Aided Diagnosis",
"Applications -> Image Synthesis / Augmentation / Super-Resolution",
"Machine Learning -> Evaluation / Benchmarking /... | [
"https://github.com/DHSeo11/AURORA.git"
] | [
"https://physionet.org/content/ptb-xl/1.0.3/",
"https://physionet.org/content/ecg-arrhythmia/1.0.0/"
] | null | @InProceedings{SeoDon_Accurate_MICCAI2026,
author = { Seo, Dong-hyuk AND Kim, Ui Jong AND Jo, Yong-Yeon AND Kwon, Junmyung AND Shin, Won-Yong AND Kim, Sang-Wook},
title = { { Accurate Reconstruction of the Standard 12-Lead ECG from a Single Lead Based on Inherent Principles of ECG } },
booktitle... | Wearable and portable devices enable convenient electrocardiography (ECG) acquisition, yet they typically measure only a single limb lead, limiting diagnostic utility compared to the standard 12-lead ECG used in hospitals. We study the problem of generating the full standard 12-lead ECG from a single measured lead by l... | null | null |
Paper4031 | ACMap: Across-scales Connectivity Mapping of Primate Brain | [
"Runjia Lin",
"Tianjia Zhu",
"Minhui Ouyang",
"Hao Huang"
] | https://papers.miccai.org/miccai-2026/0024-Paper4031.html | https://papers.miccai.org/miccai-2026/paper/4031_paper.pdf | null | null | null | [
"Body -> Brain",
"Modalities -> Diffusion MRI",
"Modalities -> Microscopy",
"Modalities -> Other",
"Applications -> Computational Anatomy & Physiology",
"Applications -> Connectivity & Network Analysis",
"Applications -> Visualization in Biomedical Imaging"
] | [] | [] | null | @InProceedings{LinRun_ACMap_MICCAI2026,
author = { Lin, Runjia AND Zhu, Tianjia AND Ouyang, Minhui AND Huang, Hao},
title = { { ACMap: Across-scales Connectivity Mapping of Primate Brain } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026},
year = ... | Brain connectivity emerges from multiple scales, from mesoscale axonal projections to macroscale white matter (WM) tract pathways. Virus tracing provides gold standard axonal projection at μm level but is restricted by limited virus injection locations and lack of streamlines. Diffusion MRI (dMRI) can cover brain-wide ... | null | null |
Paper5702 | AC-MIL: Weakly-Supervised Atrial LGE-MRI Quality Assessment via Adversarial Concept Disentanglement | [
"K. M. Arefeen Sultan",
"Kaysen Hansen",
"Benjamin Orkild",
"Alan Morris",
"Eugene Kholmovski",
"Erik Bieging",
"Eugene Kwan",
"Ravi Ranjan",
"Ed DiBella",
"Shireen Y. Elhabian"
] | https://papers.miccai.org/miccai-2026/0025-Paper5702.html | https://papers.miccai.org/miccai-2026/paper/5702_paper.pdf | null | null | null | [
"Body -> Cardiac",
"Modalities -> MRI",
"Applications -> Other",
"Machine Learning -> Interpretability / Explainability",
"Machine Learning -> Semi- / Weakly- / Self-supervised Learning"
] | [
"https://github.com/arf111/AC-MIL"
] | [] | null | @InProceedings{SulK._ACMIL_MICCAI2026,
author = { Sultan, K. M. Arefeen AND Hansen, Kaysen AND Orkild, Benjamin AND Morris, Alan AND Kholmovski, Eugene AND Bieging, Erik AND Kwan, Eugene AND Ranjan, Ravi AND DiBella, Ed AND Elhabian, Shireen Y.},
title = { { AC-MIL: Weakly-Supervised Atrial LGE-MRI Qual... | High-quality Late Gadolinium Enhancement (LGE) MRI can be helpful for atrial fibrillation management, yet scan quality is frequently compromised by patient motion, irregular breathing, and suboptimal image acquisition timing. While Multiple Instance Learning (MIL) has emerged as a powerful tool for automated quality as... | 2604.10303 | title_snapshot |
Paper2305 | Active Evaluation-Induced Graph Network Based Medical Hyperspectral Image Classification for Tumor Diagnosis | [
"Meiling Wang",
"Changda Xing",
"Yifang Wu",
"Liying Cao"
] | https://papers.miccai.org/miccai-2026/0026-Paper2305.html | https://papers.miccai.org/miccai-2026/paper/2305_paper.pdf | null | null | null | [
"Body -> Whole-body / Multi-organ / Other",
"Modalities -> Spectroscopy",
"Applications -> Computer-Aided Diagnosis",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning"
] | [] | [] | null | @InProceedings{WanMei_Active_MICCAI2026,
author = { Wang, Meiling AND Xing, Changda AND Wu, Yifang AND Cao, Liying},
title = { { Active Evaluation-Induced Graph Network Based Medical Hyperspectral Image Classification for Tumor Diagnosis } },
booktitle = {Medical Image Computing and Computer Ass... | Graph networks (GNet) have presented great potential in medical hyperspectral images (MedHSIs) for tumor diagnosis. Most of existing approaches easily suffer from the misconnection of graph inter-nodes due to tissue heterogeneity, imaging noise, and spectral aliasing included in MedHSIs. To remedy such difficulty, a no... | null | null |
Paper2507 | Active Source-free Domain Adaptation in Open-set Medical Image Segmentation via Decomposed Uncertainty and Prototype Discrepancy | [
"Jin Yang",
"Yichi Zhang",
"Peijie Qiu",
"Xiaobing Yu"
] | https://papers.miccai.org/miccai-2026/0027-Paper2507.html | https://papers.miccai.org/miccai-2026/paper/2507_paper.pdf | null | null | null | [
"Body -> Abdomen",
"Modalities -> CT / X-ray",
"Modalities -> MRI",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning",
"Machine Learning -> Domain Adaptation / Harmonization"
] | [] | [] | null | @InProceedings{YanJin_Active_MICCAI2026,
author = { Yang, Jin AND Zhang, Yichi AND Qiu, Peijie AND Yu, Xiaobing},
title = { { Active Source-free Domain Adaptation in Open-set Medical Image Segmentation via Decomposed Uncertainty and Prototype Discrepancy } },
booktitle = {Medical Image Computing... | Deep learning (DL) methods are challenged to demonstrate robust performance across different segmentation datasets due to domain shifts, but active domain adaptation techniques enhance their generalization performance by querying a few samples from target domains for adaptation training. However in clinical practice, t... | 2606.08749 | title_snapshot |
Paper2765 | AdaBIMBA: Adaptive Token Compression for Long Endoscopy Video Understanding | [
"Ka-Wai Yung",
"Pooya Mobadersany",
"Chaitanya Parmar",
"Krishna Chaitanya",
"Fabio Gunderson",
"Lindsey Surace",
"Louis R. Ghanem",
"Tommaso Mansi",
"Gabriela Oana Cula",
"Kristopher Standish",
"Pablo F. Damasceno"
] | https://papers.miccai.org/miccai-2026/0028-Paper2765.html | https://papers.miccai.org/miccai-2026/paper/2765_paper.pdf | null | null | null | [
"Body -> Abdomen",
"Modalities -> Endoscopy",
"Applications -> Computer-Aided Diagnosis",
"Machine Learning -> Multimodal Models / LLMs / VLMs"
] | [] | [] | null | @InProceedings{YunKa_AdaBIMBA_MICCAI2026,
author = { Yung, Ka-Wai AND Mobadersany, Pooya AND Parmar, Chaitanya AND Chaitanya, Krishna AND Gunderson, Fabio AND Surace, Lindsey AND Ghanem, Louis R. AND Mansi, Tommaso AND Cula, Gabriela Oana AND Standish, Kristopher AND Damasceno, Pablo F.},
title = { { Ad... | Vision–language models (VLMs) show strong potential for automating endoscopy‑video interpretation, including detailed description, disease‑severity assessment, and temporal event localization. However, most surgery and endoscopy focused VLMs remain trained primarily on static images, despite reliable assessment requiri... | null | null |
Paper5721 | ADAPT: Adaptive Profiling Transformers for Efficient Alzheimer’s Disease Diagnosis | [
"Yifeng Wang",
"Ke Chen",
"Haohan Wang"
] | https://papers.miccai.org/miccai-2026/0029-Paper5721.html | https://papers.miccai.org/miccai-2026/paper/5721_paper.pdf | null | null | null | [
"Body -> Brain",
"Modalities -> MRI",
"Applications -> Computer-Aided Diagnosis",
"Machine Learning -> Deep Learning"
] | [] | [] | null | @InProceedings{WanYif_ADAPT_MICCAI2026,
author = { Wang, Yifeng AND Chen, Ke AND Wang, Haohan},
title = { { ADAPT: Adaptive Profiling Transformers for Efficient Alzheimer’s Disease Diagnosis } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026},
yea... | Accurate and computationally efficient diagnosis of Alzheimer’s Disease (AD) from MRI remains a challenge in medical imaging. While 3D deep learning models capture volumetric biomarkers, they require substantial GPU memory. Conversely, 2D approaches are efficient but fail to capture spatial context, limiting their clin... | 2401.06349 | title_judge |
Paper0916 | Adapting Ordinal-Risk Alignment for Clinically Safe Diabetic Retinopathy Grading | [
"Yuhan Zhang",
"Xi Wang",
"Mingchao Li",
"Xiao Ma",
"Wufeng Xue",
"Dong Ni"
] | https://papers.miccai.org/miccai-2026/0030-Paper0916.html | https://papers.miccai.org/miccai-2026/paper/0916_paper.pdf | null | null | null | [
"Body -> Eye",
"Modalities -> Photograph / Video",
"Applications -> Computer-Aided Diagnosis",
"Machine Learning -> Deep Learning",
"Machine Learning -> Fairness / Safety / Reliability",
"Machine Learning -> Reinforcement Learning / Control / Decision-Making"
] | [] | [] | null | @InProceedings{ZhaYuh_Adapting_MICCAI2026,
author = { Zhang, Yuhan AND Wang, Xi AND Li, Mingchao AND Ma, Xiao AND Xue, Wufeng AND Ni, Dong},
title = { { Adapting Ordinal-Risk Alignment for Clinically Safe Diabetic Retinopathy Grading } },
booktitle = {Medical Image Computing and Computer Assiste... | Automatic diabetic retinopathy (DR) grading is essential for scalable screening and timely referral to prevent vision loss. However, most existing methods treat DR grading as a multi-class classification problem, overlooking the ordinal nature of disease severity and the asymmetric clinical risk, where underestimation ... | null | null |
Paper3550 | Adapting Without Access: Black-Box Bayesian Adaptation for Trustworthy Cross-Center Medical Image Segmentation | [
"Xiaoxiang Han",
"Yiman Liu",
"Lixin Xu",
"Meng Wang",
"Xiang Xu",
"Yan Wang",
"Yuqi Zhang",
"Qi Zhang"
] | https://papers.miccai.org/miccai-2026/0031-Paper3550.html | https://papers.miccai.org/miccai-2026/paper/3550_paper.pdf | null | null | null | [
"Body -> Abdomen",
"Body -> Cardiac",
"Modalities -> CT / X-ray",
"Modalities -> Ultrasound",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning",
"Machine Learning -> Domain Adaptation / Harmonization"
] | [] | [] | null | @InProceedings{HanXia_Adapting_MICCAI2026,
author = { Han, Xiaoxiang AND Liu, Yiman AND Xu, Lixin AND Wang, Meng AND Xu, Xiang AND Wang, Yan AND Zhang, Yuqi AND Zhang, Qi},
title = { { Adapting Without Access: Black-Box Bayesian Adaptation for Trustworthy Cross-Center Medical Image Segmentation } },
... | Cross-center deployment of medical image segmentation models faces dual challenges: domain shifts from varying imaging protocols and strict clinical constraints on privacy, security, and trustworthiness. Most existing domain adaptation methods require access to source-domain data or model parameters, which may cause pr... | null | null |
Paper4551 | Adaptive Frequency-Guided Parallel Mamba Network for Polyp Segmentation | [
"Aiwen Jiang",
"Hongqian Yu",
"Xue Li",
"Zhiqiang Xu",
"Rui Cui"
] | https://papers.miccai.org/miccai-2026/0032-Paper4551.html | https://papers.miccai.org/miccai-2026/paper/4551_paper.pdf | null | null | null | [
"Body -> Abdomen",
"Modalities -> Mobile / Wearable / Point-of-Care Imaging & Sensors",
"Modalities -> Photograph / Video",
"Applications -> Anomaly / Lesion Detection",
"Applications -> Computer-Aided Diagnosis",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning",
"Machine Lear... | [] | [] | null | @InProceedings{JiaAiw_Adaptive_MICCAI2026,
author = { Jiang, Aiwen AND Yu, Hongqian AND Li, Xue AND Xu, Zhiqiang AND Cui, Rui},
title = { { Adaptive Frequency-Guided Parallel Mamba Network for Polyp Segmentation } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICC... | Accurate polyp segmentation is crucial for early detection and timely prevention of cancer. Although existing methods have achieved remarkable progress, they still struggle in real-world clinical scenarios due to strong polyp heterogeneity, fuzzy boundaries, and the high risk of missing small polyps. To address the cha... | null | null |
Paper4611 | AdaSurvMamba: Dynamic Fusion and Semantic Scanning for Multimodal Survival Analysis | [
"Jialong Zhong",
"Tingwei Liu",
"Baokun Yue",
"Jingjing Li",
"Yongri Piao",
"Miao Zhang",
"Leiye Liu",
"Jiahong Jiang",
"Wei Ji",
"Huchuan Lu"
] | https://papers.miccai.org/miccai-2026/0033-Paper4611.html | https://papers.miccai.org/miccai-2026/paper/4611_paper.pdf | null | null | null | [
"Body -> Breast",
"Body -> Lung / Thoracic",
"Modalities -> Digital Pathology/Histopathology",
"Modalities -> Multimodal Sensor Fusion",
"Applications -> Computational / Integrative Pathology",
"Applications -> Multimodal Integration with Clinical / Genomic / Biomarkers",
"Applications -> Outcome Predic... | [
"https://github.com/zjlGO/AdaSurvMamba"
] | [] | null | @InProceedings{ZhoJia_AdaSurvMamba_MICCAI2026,
author = { Zhong, Jialong AND Liu, Tingwei AND Yue, Baokun AND Li, Jingjing AND Piao, Yongri AND Zhang, Miao AND Liu, Leiye AND Jiang, Jiahong AND Ji, Wei AND Lu, Huchuan},
title = { { AdaSurvMamba: Dynamic Fusion and Semantic Scanning for Multimodal Surviv... | Multimodal survival analysis utilizing whole slide images (WSIs) and genomic profiles is fundamental for cancer prognosis. Recently, state-space models like Mamba have emerged as powerful tools for sequence modeling. However, translating this success to complex multimodal tasks is hindered by two critical limitations. ... | 2607.16260 | title_snapshot |
Paper3644 | Addressing Gradient Conflicts in Multimodal Fundus Disease Recognition with Fusion-Guided Learning | [
"Xiaozhou Liu",
"Zhike Qiu",
"Luping Zeng",
"Jiahui Hu",
"Jiahao Liu",
"Liangming Wen",
"Zhiguo Du"
] | https://papers.miccai.org/miccai-2026/0034-Paper3644.html | https://papers.miccai.org/miccai-2026/paper/3644_paper.pdf | null | null | null | [
"Body -> Eye",
"Modalities -> Photograph / Video",
"Applications -> Computer-Aided Diagnosis",
"Machine Learning -> Deep Learning",
"Machine Learning -> Multimodal Models / LLMs / VLMs"
] | [
"https://github.com/lxz8763/FGDL-Fundus"
] | [] | null | @InProceedings{LiuXia_Addressing_MICCAI2026,
author = { Liu, Xiaozhou AND Qiu, Zhike AND Zeng, Luping AND Hu, Jiahui AND Liu, Jiahao AND Wen, Liangming AND Du, Zhiguo},
title = { { Addressing Gradient Conflicts in Multimodal Fundus Disease Recognition with Fusion-Guided Learning } },
booktitle =... | Multi-modal fundus image analysis holds significant clinical value for the early diagnosis of ophthalmic diseases. While existing studies on multi-modal diagnosis predominantly focus on designing complex fusion architectures, they often overlook the underlying optimization dynamics during joint training. In this work, ... | null | null |
Paper3002 | Addressing Tissue and Appearance Heterogeneity in Text-Guided Few-Shot WSI Classification | [
"Yongcen Li",
"Zelin Xu",
"Xiaoyu Shi",
"Zhiyi Huang",
"Xinchen Ye",
"Miao Zhang",
"Zhihui Wang",
"Rui Xu",
"Yen-Wei Chen",
"Xudong Xing"
] | https://papers.miccai.org/miccai-2026/0035-Paper3002.html | https://papers.miccai.org/miccai-2026/paper/3002_paper.pdf | null | null | null | [
"Body -> Breast",
"Body -> Lung / Thoracic",
"Modalities -> Digital Pathology/Histopathology",
"Applications -> Computational / Integrative Pathology"
] | [] | [] | null | @InProceedings{LiYon_Addressing_MICCAI2026,
author = { Li, Yongcen AND Xu, Zelin AND Shi, Xiaoyu AND Huang, Zhiyi AND Ye, Xinchen AND Zhang, Miao AND Wang, Zhihui AND Xu, Rui AND Chen, Yen-Wei AND Xing, Xudong},
title = { { Addressing Tissue and Appearance Heterogeneity in Text-Guided Few-Shot WSI Class... | In text-guided few-shot whole-slide image (WSI) classification, multiple instance learning (MIL) methods can leverage vision–language priors with very limited annotations, yet they still lag behind strongly supervised baselines. We empirically show that this gap is largely caused by slide-to-slide domain shifts induced... | null | null |
Paper0597 | AEGIS: Anatomy-Embedded Group-Invariant Segmentation for Fair Medical Foundation Models | [
"Sen Wang",
"Zhaoyi Zhan",
"Zheng Xing",
"Minqing Zhang",
"Chloe Meng Jiang",
"Zhiliang Wang",
"Ruimao Zhang",
"Xiaohui Duan",
"Weibing Zhao"
] | https://papers.miccai.org/miccai-2026/0036-Paper0597.html | https://papers.miccai.org/miccai-2026/paper/0597_paper.pdf | null | null | null | [
"Body -> Head & Neck",
"Modalities -> MRI",
"Applications -> Computer-Aided Diagnosis",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning",
"Machine Learning -> Fairness / Safety / Reliability",
"Machine Learning -> Foundation Models",
"Machine Learning -> Multimodal Models / LL... | [
"https://github.com/lettuce09/AEGIS"
] | [
"https://pi-cai.grand-challenge.org/",
"https://github.com/luoyan407/FairSeg"
] | null | @InProceedings{WanSen_AEGIS_MICCAI2026,
author = { Wang, Sen AND Zhan, Zhaoyi AND Xing, Zheng AND Zhang, Minqing AND Jiang, Chloe Meng AND Wang, Zhiliang AND Zhang, Ruimao AND Duan, Xiaohui AND Zhao, Weibing},
title = { { AEGIS: Anatomy-Embedded Group-Invariant Segmentation for Fair Medical Foundation M... | While foundation models like SAM have advanced medical image segmentation, recent evidence raises safety concerns about subgroup disparities in their medical adaptation. Existing fairness strategies face a critical dilemma when adapting these massive models via Parameter-Efficient Fine-Tuning (PEFT): objective reweight... | null | null |
Paper5918 | AffordTissue: Dense Affordance Prediction for Tool-Action Specific Tissue Interaction | [
"Aiza Maksutova",
"Lalithkumar Seenivasan",
"Hao Ding",
"Jiru Xu",
"Chenhao Yu",
"Chenyan Jing",
"Yiqing Shen",
"Mathias Unberath"
] | https://papers.miccai.org/miccai-2026/0037-Paper5918.html | https://papers.miccai.org/miccai-2026/paper/5918_paper.pdf | null | null | null | [
"Body -> Abdomen",
"Modalities -> Endoscopy",
"Applications -> Image Segmentation",
"Applications -> Image-Guided Interventions",
"Machine Learning -> Deep Learning",
"Machine Learning -> Fairness / Safety / Reliability",
"Machine Learning -> Multimodal Models / LLMs / VLMs",
"Surgery -> Planning & Si... | [] | [] | null | @InProceedings{MakAiz_AffordTissue_MICCAI2026,
author = { Maksutova, Aiza AND Seenivasan, Lalithkumar AND Ding, Hao AND Xu, Jiru AND Yu, Chenhao AND Jing, Chenyan AND Shen, Yiqing AND Unberath, Mathias},
title = { { AffordTissue: Dense Affordance Prediction for Tool-Action Specific Tissue Interaction } ... | Surgical action automation has progressed rapidly toward achieving surgeon-like dexterous control, driven primarily by advances in learning from demonstration and vision-language-action models. While these have demonstrated success in table-top experiments, translating them to clinical deployment remains challenging: c... | 2604.01371 | title_snapshot |
Paper0598 | Age Conditional Longitudinal Forecasting of Adolescent Functional Connectivity via Brownian Bridge Diffusion Models | [
"Rongye Zhang",
"Shasha Xu",
"Xiaobo Liu",
"Jing Bian",
"Rui Cao",
"Xin Wen"
] | https://papers.miccai.org/miccai-2026/0038-Paper0598.html | https://papers.miccai.org/miccai-2026/paper/0598_paper.pdf | null | null | null | [
"Body -> Brain",
"Modalities -> Functional MRI",
"Applications -> Connectivity & Network Analysis",
"Applications -> Outcome Prediction / Prognosis / Longitudinal Modeling",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/rongyezhang18-eng/BBdiffusion-FC"
] | [
"https://www.scidb.cn/en/detail?dataSetId=c81f0e90a51b4cfca348ce4da6ca734e"
] | null | @InProceedings{ZhaRon_Age_MICCAI2026,
author = { Zhang, Rongye AND Xu, Shasha AND Liu, Xiaobo AND Bian, Jing AND Cao, Rui AND Wen, Xin},
title = { { Age Conditional Longitudinal Forecasting of Adolescent Functional Connectivity via Brownian Bridge Diffusion Models } },
booktitle = {Medical Image... | To predict and generate developmental changes in brain functional connectivity (FC), we propose a framework combining a Graph Attention Autoencoder with Brownian Bridge diffusion. The autoencoder encodes FC into low dimensional features that preserve topology, while the diffusion model anchors the source and target dom... | null | null |
Paper5366 | AGE-MIL: Anchor-Guided Evidence Learning for Patient-Level Prediction | [
"Jiawei Niu",
"Jian Chen",
"Di Zhang",
"Junbo Lu",
"Zhangcheng Liao",
"Xuhao Liu",
"Honglin Zhong",
"Mireia Crispin-Ortuzar",
"Chen Li",
"Zeyu Gao",
"Yi Cai"
] | https://papers.miccai.org/miccai-2026/0039-Paper5366.html | https://papers.miccai.org/miccai-2026/paper/5366_paper.pdf | null | null | null | [
"Body -> Pelvis / Reproductive System",
"Modalities -> Digital Pathology/Histopathology",
"Applications -> Computational / Integrative Pathology"
] | [
"https://github.com/wodeniua/AGE-MIL"
] | [] | null | @InProceedings{NiuJia_AGEMIL_MICCAI2026,
author = { Niu, Jiawei AND Chen, Jian AND Zhang, Di AND Lu, Junbo AND Liao, Zhangcheng AND Liu, Xuhao AND Zhong, Honglin AND Crispin-Ortuzar, Mireia AND Li, Chen AND Gao, Zeyu AND Cai, Yi},
title = { { AGE-MIL: Anchor-Guided Evidence Learning for Patient-Level Pr... | Existing computational pathology methods predominantly operate within whole-slide image (WSI)-level multiple instance learning (MIL) paradigms, while patient-level modeling remains underexplored. In routine pathological practice, however, pathologists derive diagnostic and prognostic conclusions by integrating evidence... | 2606.12126 | title_snapshot |
Paper5424 | Agent-Guided Relational Concept Discovery: Toward Interpretable Surgical Margin Assessment | [
"Nooshin Maghsoodi",
"Amoon Jamzad",
"Robert Policelli",
"Mohammad Farahmand",
"Dilakshan Srikanthan",
"Martin Kaufmann",
"Kevin Y. M. Ren",
"Shaila Merchant",
"Sonal Varma",
"Ross Walker",
"Doug McKay",
"John Rudan",
"Gabor Fichtinger",
"Parvin Mousavi"
] | https://papers.miccai.org/miccai-2026/0040-Paper5424.html | https://papers.miccai.org/miccai-2026/paper/5424_paper.pdf | null | null | null | [
"Body -> Breast",
"Body -> Skin",
"Modalities -> Other",
"Applications -> Anomaly / Lesion Detection",
"Applications -> Medical Robotics",
"Machine Learning -> Foundation Models",
"Machine Learning -> Interpretability / Explainability",
"Machine Learning -> Multimodal Models / LLMs / VLMs"
] | [
"https://github.com/nooshinmaghsoodi/cem-qwen-reims"
] | [] | null | @InProceedings{MagNoo_AgentGuided_MICCAI2026,
author = { Maghsoodi, Nooshin AND Jamzad, Amoon AND Policelli, Robert AND Farahmand, Mohammad AND Srikanthan, Dilakshan AND Kaufmann, Martin AND Ren, Kevin Y. M. AND Merchant, Shaila AND Varma, Sonal AND Walker, Ross AND McKay, Doug AND Rudan, John AND Fichtinger, G... | Deep learning models can effectively use Rapid Evaporative Ionization Mass Spectrometry (REIMS) data for surgical margin assessment. However, their clinical adoption remains challenging due to limited generalization to operating room conditions. This difficulty arises because models are typically trained on labeled spe... | 2607.21437 | title_snapshot |
Paper3725 | AGGRNet: Selective Feature Extraction and Aggregation for Enhanced Medical Image Classification | [
"Ansh Makwe",
"Akansh Agrawal",
"Prateek Jain",
"Akshan Agrawal",
"Priyanka Bagade"
] | https://papers.miccai.org/miccai-2026/0041-Paper3725.html | https://papers.miccai.org/miccai-2026/paper/3725_paper.pdf | null | null | null | [
"Body -> Abdomen",
"Body -> Eye",
"Body -> Skin",
"Modalities -> Digital Pathology/Histopathology",
"Modalities -> Endoscopy",
"Modalities -> Other",
"Modalities -> Photograph / Video",
"Applications -> Anomaly / Lesion Detection",
"Applications -> Outcome Prediction / Prognosis / Longitudinal Model... | [
"https://github.com/AnshMakwe/AGGRNet-Feature-Extraction-and-Aggregation"
] | [] | null | @InProceedings{MakAns_AGGRNet_MICCAI2026,
author = { Makwe, Ansh AND Agrawal, Akansh AND Jain, Prateek AND Agrawal, Akshan AND Bagade, Priyanka},
title = { { AGGRNet: Selective Feature Extraction and Aggregation for Enhanced Medical Image Classification } },
booktitle = {Medical Image Computing ... | Medical image analysis for complex tasks such as severity grading and disease subtype classification poses significant challenges due to intricate and similar visual patterns among classes, scarcity of labeled data, and variability in expert interpretations. Although deep learning models can capture complex visual patt... | 2511.12382 | title_snapshot |
Paper3186 | AHPR-Net: Anatomy-aware Hierarchical Prompting Refinement Network for Spine Image Segmentation | [
"Junyong Zhao",
"Kun Wang",
"Dingwei Fan",
"Qi Dou",
"Liang Sun",
"Daoqiang Zhang"
] | https://papers.miccai.org/miccai-2026/0042-Paper3186.html | https://papers.miccai.org/miccai-2026/paper/3186_paper.pdf | null | null | null | [
"Body -> Spine",
"Modalities -> CT / X-ray",
"Modalities -> MRI",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/zjy399/AHPR-Net"
] | [] | null | @InProceedings{ZhaJun_AHPRNet_MICCAI2026,
author = { Zhao, Junyong AND Wang, Kun AND Fan, Dingwei AND Dou, Qi AND Sun, Liang AND Zhang, Daoqiang},
title = { { AHPR-Net: Anatomy-aware Hierarchical Prompting Refinement Network for Spine Image Segmentation } },
booktitle = {Medical Image Computing ... | Accurate spine segmentation is pivotal for surgical planning, yet remains hindered by complex anatomical structures and ambiguous boundaries. Recently, Segment Anything Model (SAM)-based methods have shown impressive generalization. However, single-level prompts often fail to fully capture semantic discriminability and... | null | null |
Paper5697 | AI-Driven Pulmonary Congestion Assessment for Lung Ultrasound via Segmentation-Guided Transformers | [
"Fahimeh Fooladgar",
"Deepa Krishnaswamy",
"Tamas Ungi",
"Viet Dinh",
"Mike Jin",
"Matheus Alves",
"Caroline Schissel",
"Shreyas Puducheri",
"Shuhei Misawa",
"Erik Duhaime",
"Stephen Hallisey",
"Alejandra Duran Mendicuti",
"Nicole Duggan",
"Purang Abolmaesumi",
"Nicholas Harrison",
"An... | https://papers.miccai.org/miccai-2026/0043-Paper5697.html | https://papers.miccai.org/miccai-2026/paper/5697_paper.pdf | null | null | null | [
"Body -> Lung / Thoracic",
"Modalities -> Ultrasound",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/Fahim-F/AI-Driven-B-Line-Detection"
] | [
"https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/HFTV7D"
] | null | @InProceedings{FooFah_AIDriven_MICCAI2026,
author = { Fooladgar, Fahimeh AND Krishnaswamy, Deepa AND Ungi, Tamas AND Dinh, Viet AND Jin, Mike AND Alves, Matheus AND Schissel, Caroline AND Puducheri, Shreyas AND Misawa, Shuhei AND Duhaime, Erik AND Hallisey, Stephen AND Duran Mendicuti, Alejandra AND Duggan, Nic... | Point-of-care lung ultrasound has emerged as a valuable bedside tool for diagnosing pulmonary congestion, which is often identified by the presence of hyperechoic reverberation artifacts called B-lines. However, manual B-line quantification is limited by substantial inter-operator variability and the requirement for sp... | null | null |
Paper0037 | ALFA: Biplanar X-Ray Reconstruction via Anatomy-Latent Field Adaptation | [
"Tianqi Yu",
"Chenhe Du",
"Jie Wen",
"Hongjiang Wei",
"Yuyao Zhang"
] | https://papers.miccai.org/miccai-2026/0044-Paper0037.html | https://papers.miccai.org/miccai-2026/paper/0037_paper.pdf | null | null | null | [
"Body -> Head & Neck",
"Modalities -> CT / X-ray",
"Applications -> Image Reconstruction",
"Machine Learning -> Other",
"Special Topic -> Low-Cost / Point-of-Care Imaging"
] | [] | [] | null | @InProceedings{YuTia_ALFA_MICCAI2026,
author = { Yu, Tianqi AND Du, Chenhe AND Wen, Jie AND Wei, Hongjiang AND Zhang, Yuyao},
title = { { ALFA: Biplanar X-Ray Reconstruction via Anatomy-Latent Field Adaptation } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI... | Biplanar X-ray 3D reconstruction offers a low-cost, low-dose alternative to computed tomography (CT) by inferring volumetric anatomy from two orthogonal projections. However, this task is highly ill-posed due to severe information loss. Existing methods either hallucinate missing structures via generative priors or lea... | null | null |
Paper2958 | Aligning Prototypes and Updating Null Spaces for Continual WSI Learning | [
"Xianrui Li",
"Kaiwen Xiao",
"Antoni B. Chan"
] | https://papers.miccai.org/miccai-2026/0045-Paper2958.html | https://papers.miccai.org/miccai-2026/paper/2958_paper.pdf | null | null | null | [
"Body -> Whole-body / Multi-organ / Other",
"Modalities -> Digital Pathology/Histopathology",
"Applications -> Computer-Aided Diagnosis",
"Machine Learning -> Continual / Lifelong Learning",
"Machine Learning -> Semi- / Weakly- / Self-supervised Learning"
] | [] | [] | null | @InProceedings{LiXia_Aligning_MICCAI2026,
author = { Li, Xianrui AND Xiao, Kaiwen AND Chan, Antoni B.},
title = { { Aligning Prototypes and Updating Null Spaces for Continual WSI Learning } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026},
year =... | Whole Slide Image (WSI) analysis in digital pathology requires continual learning (CL) to adapt to growing data streams without catastrophic forgetting. Current CL methods for WSIs face challenges from large sample sizes and strict patient privacy constraints, risking either data leakage or limited performance on visua... | null | null |
Paper5292 | All-in-One Augmented Reality Guided Head and Neck Tumor Resection | [
"Yue Yang",
"Matthieu Chabanas",
"Carrie Reale",
"Annie Benson",
"Jason Slagle",
"Matthew Weinger",
"Michael Topf",
"Jie Ying Wu"
] | https://papers.miccai.org/miccai-2026/0046-Paper5292.html | https://papers.miccai.org/miccai-2026/paper/5292_paper.pdf | null | null | null | [
"Body -> Head & Neck",
"Modalities -> Mobile / Wearable / Point-of-Care Imaging & Sensors",
"Applications -> Image-Guided Interventions",
"Machine Learning -> Evaluation / Benchmarking / Reproducibility",
"Surgery -> Mixed / Augmented / Virtual Reality",
"Surgery -> Navigation"
] | [] | [] | null | @InProceedings{YanYue_AllinOne_MICCAI2026,
author = { Yang, Yue AND Chabanas, Matthieu AND Reale, Carrie AND Benson, Annie AND Slagle, Jason AND Weinger, Matthew AND Topf, Michael AND Wu, Jie Ying},
title = { { All-in-One Augmented Reality Guided Head and Neck Tumor Resection } },
booktitle = {M... | Positive margins are common in head and neck squamous cell carcinoma, yet intraoperative re-resection is often imprecise because margin locations are typically communicated verbally from pathology. We present an all-in-one augmented reality (AR) system that relocalizes positive margins from a resected specimen to the r... | 2603.29495 | title_snapshot |
Paper0535 | ALSAnchorNet: Biologically Informed Multimodal MRI Fusion for Amyotrophic Lateral Sclerosis Diagnosis | [
"Xiongri Shen",
"Jixin Luan",
"Xingcan Hu",
"Le Lu",
"Fuchen Liu",
"Shuangwu Liu",
"Long Xie"
] | https://papers.miccai.org/miccai-2026/0047-Paper0535.html | https://papers.miccai.org/miccai-2026/paper/0535_paper.pdf | null | null | null | [
"Body -> Brain",
"Modalities -> Diffusion MRI",
"Modalities -> Functional MRI",
"Modalities -> MRI",
"Applications -> Computer-Aided Diagnosis",
"Machine Learning -> Deep Learning",
"Special Topic -> AI for Neglected Diseases & Limited-Resource Settings"
] | [
"https://github.com/AQ-MedAI/ALSAnchorNet"
] | [
"https://github.com/AQ-MedAI/ALSAnchorNet"
] | null | @InProceedings{SheXio_ALSAnchorNet_MICCAI2026,
author = { Shen, Xiongri AND Luan, Jixin AND Hu, Xingcan AND Lu, Le AND Liu, Fuchen AND Liu, Shuangwu AND Xie, Long},
title = { { ALSAnchorNet: Biologically Informed Multimodal MRI Fusion for Amyotrophic Lateral Sclerosis Diagnosis } },
booktitle = ... | Deep learning–based multimodal MRI integration for Amyotrophic Lateral Sclerosis (ALS) diagnosis remains largely unexplored. We present ALSAnchorNet, a biologically guided multimodal framework that integrates resting-state fMRI, diffusion tensor imaging, and structural MRI for ALS diagnosis. We introduce Frontal Anchor... | null | null |
Paper4880 | AMFG: Anatomy-Grounded Multi-finding Guidance for Training-Free Chest X-Ray Generation | [
"Yeon Gyu Han",
"Junah Jung",
"Chang Min Park",
"Dongheon Lee"
] | https://papers.miccai.org/miccai-2026/0048-Paper4880.html | https://papers.miccai.org/miccai-2026/paper/4880_paper.pdf | null | null | null | [
"Body -> Lung / Thoracic",
"Modalities -> CT / X-ray",
"Applications -> Image Synthesis / Augmentation / Super-Resolution",
"Machine Learning -> Deep Learning",
"Machine Learning -> Foundation Models",
"Machine Learning -> Multimodal Models / LLMs / VLMs",
"Machine Learning -> Reinforcement Learning / C... | [] | [
"https://physionet.org/content/mimic-cxr/"
] | null | @InProceedings{HanYeo_AMFG_MICCAI2026,
author = { Han, Yeon Gyu AND Jung, Junah AND Park, Chang Min AND Lee, Dongheon},
title = { { AMFG: Anatomy-Grounded Multi-finding Guidance for Training-Free Chest X-Ray Generation } },
booktitle = {Medical Image Computing and Computer Assisted Intervention ... | Text-conditioned diffusion models have enabled chest X-ray (CXR) synthesis, yet when prompted with multi-finding radiology reports they frequently omit findings or place them at anatomically incorrect locations. Existing approaches improve this through reinforcement learning or adapter training, but their reliance on a... | null | null |
Paper2038 | An Artifact-Based Agent Framework for Adaptive and Reproducible Medical Image Processing | [
"Lianrui Zuo",
"Yihao Liu",
"Gaurav Rudravaram",
"Karthik Ramadass",
"Aravind R. Krishnan",
"Michael D. Phillips",
"Yelena G. Bodien",
"Mayur B. Patel",
"Paula Trujillo",
"Yency Forero Martinez",
"Stephen A. Deppen",
"Eric L. Grogan",
"Fabien Maldonado",
"Kevin McGann",
"Hudson M. Holmes... | https://papers.miccai.org/miccai-2026/0049-Paper2038.html | https://papers.miccai.org/miccai-2026/paper/2038_paper.pdf | null | null | null | [
"Body -> Brain",
"Body -> Lung / Thoracic",
"Modalities -> CT / X-ray",
"Modalities -> MRI",
"Applications -> Other",
"Applications -> Visualization in Biomedical Imaging",
"Machine Learning -> Other",
"Special Topic -> Clinical Trials & Deployment"
] | [
"https://github.com/MASILab/medimg-agent"
] | [
"https://www.cancerimagingarchive.net/collection/nlst/"
] | null | @InProceedings{ZuoLia_An_MICCAI2026,
author = { Zuo, Lianrui AND Liu, Yihao AND Rudravaram, Gaurav AND Ramadass, Karthik AND Krishnan, Aravind R. AND Phillips, Michael D. AND Bodien, Yelena G. AND Patel, Mayur B. AND Trujillo, Paula AND Forero Martinez, Yency AND Deppen, Stephen A. AND Grogan, Eric L. AND Maldo... | Medical imaging research is increasingly shifting from controlled benchmark evaluation toward real-world clinical deployment. In such settings, applying analytical methods extends beyond model design and requires dataset-aware workflow configuration and provenance tracking. Two requirements therefore become central: ad... | 2604.21936 | title_snapshot |
Paper4807 | Anatomically Accurate 3D Vessel Generation via 3-Phase Chebyshev Curve Diffusion | [
"Jihwan Mo",
"Sangbaek Yoo",
"Jaesoon Choi",
"Dong Eui Chang"
] | https://papers.miccai.org/miccai-2026/0050-Paper4807.html | https://papers.miccai.org/miccai-2026/paper/4807_paper.pdf | null | null | null | [
"Body -> Cardiac",
"Body -> Vasculature",
"Modalities -> CT / X-ray",
"Modalities -> Synthetic / Computational Imaging",
"Applications -> Digital Twins / Simulation / Synthetic Data",
"Applications -> Image Synthesis / Augmentation / Super-Resolution",
"Machine Learning -> Deep Learning",
"Machine Lea... | [
"https://github.com/mozzi-bro/Chebyshev_Diffusion"
] | [
"https://github.com/XiaoweiXu/ImageCAS-A-Large-Scale-Dataset-and-Benchmark-for-Coronary-Artery-Segmentation-based-on-CT"
] | null | @InProceedings{MoJih_Anatomically_MICCAI2026,
author = { Mo, Jihwan AND Yoo, Sangbaek AND Choi, Jaesoon AND Chang, Dong Eui},
title = { { Anatomically Accurate 3D Vessel Generation via 3-Phase Chebyshev Curve Diffusion } },
booktitle = {Medical Image Computing and Computer Assisted Intervention ... | Large-scale collection of 3D vascular data is inherently difficult, resulting in scarce publicly available datasets. Existing methods represent vessel edges as discrete point sequences, which cannot guarantee smoothness and lose millimeter-scale size information through normalization. We propose 3-Phase Chebyshev Curve... | null | null |
Paper0906 | Anatomically Consistent TMJ Disc Segmentation via Semantic Anchoring and Clinical Priors | [
"Dayun Ju",
"Chanyoung Kim",
"Sunyoung Jung",
"Hyo-Jung Jung",
"Chena Lee",
"Younjung Park",
"Seong Jae Hwang"
] | https://papers.miccai.org/miccai-2026/0051-Paper0906.html | https://papers.miccai.org/miccai-2026/paper/0906_paper.pdf | null | null | null | [
"Body -> Musculoskeletal / Orthopedic",
"Modalities -> MRI",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/jdy77/TISC"
] | [] | null | @InProceedings{JuDay_Anatomically_MICCAI2026,
author = { Ju, Dayun AND Kim, Chanyoung AND Jung, Sunyoung AND Jung, Hyo-Jung AND Lee, Chena AND Park, Younjung AND Hwang, Seong Jae},
title = { { Anatomically Consistent TMJ Disc Segmentation via Semantic Anchoring and Clinical Priors } },
booktitle... | Segmenting the temporomandibular joint (TMJ) disc from MRI is essential for accurate diagnosis of internal derangement, yet it remains unreliable in practice due to its small size, low contrast, and morphological variability. Existing methods, primarily adapted from general segmentation architectures, often produce fra... | 2606.21177 | title_snapshot |
Paper3157 | Anatomy- and Site-Guided 3D Patch Diffusion for Robust MRI Harmonization | [
"Barnabé Hache",
"Vincent Roca",
"Grégory Kuchcinski",
"Dorian Manouvriez",
"Renaud Lopes"
] | https://papers.miccai.org/miccai-2026/0052-Paper3157.html | https://papers.miccai.org/miccai-2026/paper/3157_paper.pdf | null | null | null | [
"Body -> Brain",
"Modalities -> MRI",
"Applications -> Image Reconstruction",
"Applications -> Image Registration",
"Applications -> Image Synthesis / Augmentation / Super-Resolution",
"Machine Learning -> Deep Learning",
"Machine Learning -> Domain Adaptation / Harmonization"
] | [
"https://github.com/barna-hache/PatchLevelDiffusionMRIHarmonization"
] | [
"https://fcon_1000.projects.nitrc.org/indi/retro/sald.html",
"https://brain-development.org/ixi-dataset/",
"https://sites.wustl.edu/oasisbrains/home/oasis-3/",
"https://adni.loni.usc.edu/aibl-australian-imaging-biomarkers-and-lifestyle-study-of-ageing-18-month-data-now-released/",
"https://www.synapse.org/S... | null | @InProceedings{HacBar_Anatomy_MICCAI2026,
author = { Hache, Barnabé AND Roca, Vincent AND Kuchcinski, Grégory AND Manouvriez, Dorian AND Lopes, Renaud},
title = { { Anatomy- and Site-Guided 3D Patch Diffusion for Robust MRI Harmonization } },
booktitle = {Medical Image Computing and Computer Ass... | Pooling multi-site T1-weighted MRI increases power but introduces scanner-driven variability that can bias analyses. We propose a 3D patch-based conditional diffusion framework for MRI harmonization that preserves fine anatomical details while aligning image contrast across sites. Our method constructs site-invariant a... | null | null |
Paper1980 | Anatomy-Anchored Self-Supervision: Distilling Vision Foundation Models for Invariant Ultrasound Representation | [
"Chunzheng Zhu",
"Yijun Wang",
"Jianxin Lin",
"Feng Wang",
"Hongwei Wang",
"Lei Zhao",
"Shengli Li",
"Kenli Li"
] | https://papers.miccai.org/miccai-2026/0053-Paper1980.html | https://papers.miccai.org/miccai-2026/paper/1980_paper.pdf | null | null | null | [
"Body -> Whole-body / Multi-organ / Other",
"Modalities -> Ultrasound",
"Applications -> Anomaly / Lesion Detection",
"Applications -> Computer-Aided Diagnosis",
"Applications -> Image Segmentation",
"Machine Learning -> Semi- / Weakly- / Self-supervised Learning"
] | [
"https://github.com/zhcz328/ANAUS"
] | [] | null | @InProceedings{ZhuChu_AnatomyAnchored_MICCAI2026,
author = { Zhu, Chunzheng AND Wang, Yijun AND Lin, Jianxin AND Wang, Feng AND Wang, Hongwei AND Zhao, Lei AND Li, Shengli AND Li, Kenli},
title = { { Anatomy-Anchored Self-Supervision: Distilling Vision Foundation Models for Invariant Ultrasound Represen... | Self-supervised pre-training paradigm has gained increasing prominence for learning transferable representations in medical imaging, yet existing methods for ultrasound (US) images operate at the image or frame level, overlooking the anatomical context for clinical-aligned representation learning. In this work, we prop... | 2605.25402 | title_snapshot |
Paper6264 | Anatomy-Aware Hierarchical Multiple Instance Learning for Interpretable COPD Diagnosis and Phenotype Analysis | [
"Raha Ahmadi",
"Don Sin",
"Stephen Lam",
"Rachel Eddy",
"Roger Tam"
] | https://papers.miccai.org/miccai-2026/0054-Paper6264.html | https://papers.miccai.org/miccai-2026/paper/6264_paper.pdf | null | null | null | [
"Body -> Lung / Thoracic",
"Modalities -> CT / X-ray",
"Applications -> Computer-Aided Diagnosis",
"Machine Learning -> Deep Learning",
"Machine Learning -> Interpretability / Explainability",
"Machine Learning -> Semi- / Weakly- / Self-supervised Learning"
] | [
"https://github.com/rahaahmadi/AnatoMIL"
] | [] | null | @InProceedings{AhmRah_AnatomyAware_MICCAI2026,
author = { Ahmadi, Raha AND Sin, Don AND Lam, Stephen AND Eddy, Rachel AND Tam, Roger},
title = { { Anatomy-Aware Hierarchical Multiple Instance Learning for Interpretable COPD Diagnosis and Phenotype Analysis } },
booktitle = {Medical Image Computi... | Chronic obstructive pulmonary disease (COPD) is a leading cause of mortality worldwide. COPD causes irreversible lung damage, making early detection essential for slowing disease progression. However, current clinical assessments primarily rely on pulmonary function tests, which require respiratory expertise and cannot... | null | null |
Paper2633 | Anatomy-Aware Prediction of Bronchoscopic Accessibility from 3D CT | [
"Linkai Peng",
"Cuiling Sun",
"Bin Wang",
"Jamie Rowell",
"Catherine Gao",
"Oyku Ikizgul",
"Eminenur Sentasci",
"Andrea Bejar",
"Halil Ertugrul Aktas",
"Gorkem Durak",
"Momen Wahidi",
"Christopher Kapp",
"Ulas Bagci"
] | https://papers.miccai.org/miccai-2026/0055-Paper2633.html | https://papers.miccai.org/miccai-2026/paper/2633_paper.pdf | null | null | null | [
"Body -> Lung / Thoracic",
"Modalities -> CT / X-ray",
"Applications -> Computer-Aided Diagnosis",
"Surgery -> Planning & Simulation"
] | [
"https://nubagcilab.github.io/BronchoAccess/"
] | [
"https://nubagcilab.github.io/BronchoAccess/"
] | null | @InProceedings{PenLin_AnatomyAware_MICCAI2026,
author = { Peng, Linkai AND Sun, Cuiling AND Wang, Bin AND Rowell, Jamie AND Gao, Catherine AND Ikizgul, Oyku AND Sentasci, Eminenur AND Bejar, Andrea AND Aktas, Halil Ertugrul AND Durak, Gorkem AND Wahidi, Momen AND Kapp, Christopher AND Bagci, Ulas},
titl... | Pre-operative planning for bronchoscopy is critical for the diagnosis of lung lesions. Current accessibility assessment relies on subjective manual inspection of CT scans, which is time-consuming and prone to inter-observer variability. In this paper, we formalize bronchoscopy accessibility prediction as a novel superv... | null | null |
Paper2755 | Anatomy-Aware Reverse Symmetric Self-paced Learning for Prostate Cancer Detection | [
"Dan Zhang",
"Di Zhao",
"Yu Sun",
"Alan Wang",
"Claude Aguergaray",
"Hayley M. Reynolds"
] | https://papers.miccai.org/miccai-2026/0056-Paper2755.html | https://papers.miccai.org/miccai-2026/paper/2755_paper.pdf | null | null | null | [
"Body -> Whole-body / Multi-organ / Other",
"Modalities -> MRI",
"Applications -> Anomaly / Lesion Detection",
"Applications -> Computer-Aided Diagnosis"
] | [] | [] | null | @InProceedings{ZhaDan_AnatomyAware_MICCAI2026,
author = { Zhang, Dan AND Zhao, Di AND Sun, Yu AND Wang, Alan AND Aguergaray, Claude AND Reynolds, Hayley M.},
title = { { Anatomy-Aware Reverse Symmetric Self-paced Learning for Prostate Cancer Detection } },
booktitle = {Medical Image Computing an... | Despite the success of deep learning models to detect clinically significant prostate cancer (csPCa) using multiparametric MRI (mpMRI), it faces two levels of data imbalance: (1) voxel-level class imbalance within each patient case, where lesion voxels occupy a small fraction of the image volume, and (2) case-level dif... | null | null |
Paper2244 | Anatomy-Aware Standard Plane Localization in 3D Ultrasound with Geometric Constraints | [
"Jianlong Nie",
"Xi Ouyang",
"Yuyu Zhou",
"Qi Wan",
"Zhong Xue",
"Xiaohuan Cao",
"Dinggang Shen"
] | https://papers.miccai.org/miccai-2026/0057-Paper2244.html | https://papers.miccai.org/miccai-2026/paper/2244_paper.pdf | null | null | null | [
"Body -> Brain",
"Body -> Fetal / Pediatric",
"Body -> Head & Neck",
"Modalities -> Ultrasound",
"Applications -> Image Reconstruction",
"Applications -> Other",
"Machine Learning -> Deep Learning",
"Surgery -> Navigation"
] | [] | [] | null | @InProceedings{NieJia_AnatomyAware_MICCAI2026,
author = { Nie, Jianlong AND Ouyang, Xi AND Zhou, Yuyu AND Wan, Qi AND Xue, Zhong AND Cao, Xiaohuan AND Shen, Dinggang},
title = { { Anatomy-Aware Standard Plane Localization in 3D Ultrasound with Geometric Constraints } },
booktitle = {Medical Imag... | Standard plane localization is a critical prerequisite in ultrasound scanning for disease diagnosis. Although three-dimensional (3D) ultrasound acquires volumetric data superior to two-dimensional (2D) imaging, manually localizing multiple standard planes simultaneously within 3D volume remains labor-intensive and pron... | null | null |
Paper2678 | Anatomy-Conditioned Domain Randomization for Zero-Shot 3D Cerebrovascular Segmentation | [
"Matteo Pentassuglia",
"Xiaoming Zhang",
"Benjamin Billot",
"Maria A. Zuluaga"
] | https://papers.miccai.org/miccai-2026/0058-Paper2678.html | https://papers.miccai.org/miccai-2026/paper/2678_paper.pdf | null | null | null | [
"Body -> Brain",
"Body -> Vasculature",
"Modalities -> CT / X-ray",
"Modalities -> MRI",
"Modalities -> Synthetic / Computational Imaging",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning",
"Machine Learning -> Domain Adaptation / Harmonization",
"Machine Learning -> Synthet... | [
"https://github.com/erc-caravel/anatomical_vessel_dr"
] | [] | null | @InProceedings{PenMat_AnatomyConditioned_MICCAI2026,
author = { Pentassuglia, Matteo AND Zhang, Xiaoming AND Billot, Benjamin AND Zuluaga, Maria A.},
title = { { Anatomy-Conditioned Domain Randomization for Zero-Shot 3D Cerebrovascular Segmentation } },
booktitle = {Medical Image Computing and C... | Domain randomization methods have shown strong cross-domain robustness in neuroimaging, yet their use for 3D cerebrovascular segmentation remains limited. Existing vascular synthetic pipelines largely model vessel morphology and appearance in isolation, with limited explicit control of organ-level anatomical context du... | null | null |
Paper6167 | Anatomy-Grounded Synthetic Coronary Angiography for Geometry-Informed Multi-view Matching | [
"In Kyu Lee",
"Sumin Seo",
"Jaesik Min"
] | https://papers.miccai.org/miccai-2026/0059-Paper6167.html | https://papers.miccai.org/miccai-2026/paper/6167_paper.pdf | null | null | null | [
"Body -> Cardiac",
"Modalities -> CT / X-ray",
"Applications -> Digital Twins / Simulation / Synthetic Data",
"Applications -> Image Reconstruction",
"Machine Learning -> Deep Learning",
"Machine Learning -> Synthetic Data / Data-centric AI"
] | [
"https://github.com/medipixel/GIMM"
] | [
"https://drive.google.com/drive/folders/1JXRTGlTbWIbb-hvGqmrZnPXJZs86otZr?usp=sharing"
] | null | @InProceedings{LeeIn_AnatomyGrounded_MICCAI2026,
author = { Lee, In Kyu AND Seo, Sumin AND Min, Jaesik},
title = { { Anatomy-Grounded Synthetic Coronary Angiography for Geometry-Informed Multi-view Matching } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 20... | Accurate correspondence matching across multiple angiographic views is the prerequisite for 3D coronary reconstruction and interventional guidance. However, the development of robust deep learning models for this task has been stifled by a fundamental data bottleneck. Obtaining ground truth for matching tasks in angiog... | 2606.28474 | title_snapshot |
Paper3003 | Anatomy-Guided Residual Motion Diffusion for Controllable 4D Cardiac MRI Synthesis | [
"Yiheng Cao",
"Gustavo Andrade-Miranda",
"Jiatian Zhang",
"Lingxiao Zhao",
"Xin Gao"
] | https://papers.miccai.org/miccai-2026/0060-Paper3003.html | https://papers.miccai.org/miccai-2026/paper/3003_paper.pdf | null | null | https://papers.miccai.org/miccai-2026/supp/3003_supp.zip | [
"Body -> Cardiac",
"Modalities -> MRI",
"Applications -> Image Synthesis / Augmentation / Super-Resolution",
"Machine Learning -> Deep Learning",
"Machine Learning -> Semi- / Weakly- / Self-supervised Learning"
] | [
"https://github.com/cyiheng/4DCardiacMRISynthesis"
] | [
"https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html",
"https://www.kaggle.com/c/second-annual-data-science-bowl/data",
"https://www.ub.edu/mnms/",
"https://www.ub.edu/mnms-2/"
] | null | @InProceedings{CaoYih_AnatomyGuided_MICCAI2026,
author = { Cao, Yiheng AND Andrade-Miranda, Gustavo AND Zhang, Jiatian AND Zhao, Lingxiao AND Gao, Xin},
title = { { Anatomy-Guided Residual Motion Diffusion for Controllable 4D Cardiac MRI Synthesis } },
booktitle = {Medical Image Computing and Co... | Developing robust artificial intelligence models for 4D (3D + time) medical imaging is constrained by limited annotated data, inter-device domain shifts, and privacy restrictions. To address this, we propose a 4D controllable generative framework for anatomically consistent data augmentation. The model generates both v... | 2606.26764 | title_snapshot |
Paper4609 | Anatomy-Structured Hierarchical MIL for Weakly-Supervised Thoracic Disease Detection in Chest X-Rays | [
"Jeongin Kim",
"Sohyun Ahn",
"Seo Young Kang",
"Jaeyi Sung",
"Soomin Kim",
"Sungho Cho",
"Rena Lee",
"Kwanchang Kim",
"Junhyug Noh"
] | https://papers.miccai.org/miccai-2026/0061-Paper4609.html | https://papers.miccai.org/miccai-2026/paper/4609_paper.pdf | null | null | null | [
"Body -> Lung / Thoracic",
"Modalities -> CT / X-ray",
"Applications -> Anomaly / Lesion Detection",
"Applications -> Computer-Aided Diagnosis",
"Machine Learning -> Foundation Models",
"Machine Learning -> Interpretability / Explainability",
"Machine Learning -> Semi- / Weakly- / Self-supervised Learni... | [
"https://github.com/jn-kim/ash-mil"
] | [] | null | @InProceedings{KimJeo_AnatomyStructured_MICCAI2026,
author = { Kim, Jeongin AND Ahn, Sohyun AND Kang, Seo Young AND Sung, Jaeyi AND Kim, Soomin AND Cho, Sungho AND Lee, Rena AND Kim, Kwanchang AND Noh, Junhyug},
title = { { Anatomy-Structured Hierarchical MIL for Weakly-Supervised Thoracic Disease Detec... | Weakly-supervised thoracic disease detection in chest X-rays (CXR) is challenging due to subtle appearances and complex anatomical overlap, motivating anatomy-aware modeling for improved localization. However, prior anatomy-aware methods typically rely on coarse region proxies or static spatial priors, which may restri... | null | null |
Paper1509 | Anatomy-Texture Aware Safe Gradient Guidance for Source-Free Domain Adaptive Echocardiography Video Segmentation | [
"Jinrong Lv",
"Xun Gong",
"Ning Cheng",
"Zhaohuan Li",
"Weili Jiang"
] | https://papers.miccai.org/miccai-2026/0062-Paper1509.html | https://papers.miccai.org/miccai-2026/paper/1509_paper.pdf | null | null | null | [
"Body -> Cardiac",
"Modalities -> Ultrasound",
"Applications -> Image Segmentation",
"Machine Learning -> Deep Learning",
"Machine Learning -> Domain Adaptation / Harmonization"
] | [
"https://github.com/lvmarch/ATASGG-SFDA"
] | [
"https://www.creatis.insa-lyon.fr/Challenge/camus/",
"https://echonet.github.io/dynamic/",
"https://echonet.github.io/pediatric/"
] | null | @InProceedings{LvJin_AnatomyTexture_MICCAI2026,
author = { Lv, Jinrong AND Gong, Xun AND Cheng, Ning AND Li, Zhaohuan AND Jiang, Weili},
title = { { Anatomy-Texture Aware Safe Gradient Guidance for Source-Free Domain Adaptive Echocardiography Video Segmentation } },
booktitle = {Medical Image Co... | Source-Free Domain Adaptation (SFDA) has emerged as a crucial technique for deploying medical image segmentation models across domains, particularly when source data is inaccessible due to privacy regulations. While standard SFDA methods based on entropy minimization self-supervised learning (EMSSL) have shown promise ... | null | null |
Paper2300 | Angio-Stitch: An Unsupervised Coarse-to-Fine Framework for Sequential Stitching of Confocal Laser Endomicroscopy Images | [
"Xiaoshi Hu",
"Huahui Zhang",
"Xiang Deng",
"Xiaoyue Liu",
"Peng Wang",
"Xuesong Ye"
] | https://papers.miccai.org/miccai-2026/0063-Paper2300.html | https://papers.miccai.org/miccai-2026/paper/2300_paper.pdf | null | null | null | [
"Body -> Vasculature",
"Modalities -> Microscopy",
"Applications -> Computer-Aided Diagnosis",
"Applications -> Image Reconstruction",
"Applications -> Image Registration",
"Machine Learning -> Deep Learning"
] | [
"https://github.com/xiaoshihu8/Angio-Stitch"
] | [] | null | @InProceedings{HuXia_AngioStitch_MICCAI2026,
author = { Hu, Xiaoshi AND Zhang, Huahui AND Deng, Xiang AND Liu, Xiaoyue AND Wang, Peng AND Ye, Xuesong},
title = { { Angio-Stitch: An Unsupervised Coarse-to-Fine Framework for Sequential Stitching of Confocal Laser Endomicroscopy Images } },
booktit... | Microvascular remodeling is a critical hallmark of early-stage tumorigenesis. While Confocal Laser Endomicroscopy (CLE) enables \textit{in vivo} “optical biopsy” of the microvasculature, its restricted field of view (FoV) limits the assessment of macroscopic pathological continuity. To bridge this gap, we propose Angio... | null | null |
Paper4614 | Angular-Constrained Hyperbolic Learning for Hierarchical Multimodal Survival Prediction | [
"Haotian Yang",
"Qing Zhang",
"Qingli Li",
"Yan Wang"
] | https://papers.miccai.org/miccai-2026/0064-Paper4614.html | https://papers.miccai.org/miccai-2026/paper/4614_paper.pdf | null | null | null | [
"Body -> Whole-body / Multi-organ / Other",
"Modalities -> Microscopy",
"Applications -> Computer-Aided Diagnosis"
] | [] | [] | null | @InProceedings{YanHao_AngularConstrained_MICCAI2026,
author = { Yang, Haotian AND Zhang, Qing AND Li, Qingli AND Wang, Yan},
title = { { Angular-Constrained Hyperbolic Learning for Hierarchical Multimodal Survival Prediction } },
booktitle = {Medical Image Computing and Computer Assisted Interve... | Multimodal learning has demonstrated strong potential for cancer survival prediction through the joint modeling of whole-slide images (WSIs) and genomic data. However, existing approaches struggle to capture deep interactions between genomic signals and multiscale pathological representations. Although hyperbolic geome... | null | null |
Paper3455 | Anomaly Detection in Fetal Echocardiography via Cross-Modal Translation and Region-Discriminated Error Calibration | [
"Qianye Yang",
"Yingyu Yang",
"Yipei Wang",
"Yipeng Hu",
"Can Peng",
"Elena D’Alberti",
"Bojana Salovic",
"Amaya Iriondo-Coysh",
"Beverly Tsai-Goodman",
"Julene Carvalho",
"Fabricio da Silva Costa",
"Makrina Savvidou",
"Olga Patey",
"Aris T. Papageorghiou",
"J. Alison Noble"
] | https://papers.miccai.org/miccai-2026/0065-Paper3455.html | https://papers.miccai.org/miccai-2026/paper/3455_paper.pdf | null | null | null | [
"Body -> Cardiac",
"Body -> Fetal / Pediatric",
"Modalities -> Ultrasound",
"Applications -> Anomaly / Lesion Detection",
"Applications -> Image Reconstruction",
"Applications -> Image Synthesis / Augmentation / Super-Resolution",
"Applications -> Multimodal Integration with Clinical / Genomic / Biomark... | [
"https://github.com/QianyeYang/XCalibAD"
] | [] | null | @InProceedings{YanQia_Anomaly_MICCAI2026,
author = { Yang, Qianye AND Yang, Yingyu AND Wang, Yipei AND Hu, Yipeng AND Peng, Can AND D’Alberti, Elena AND Salovic, Bojana AND Iriondo-Coysh, Amaya AND Tsai-Goodman, Beverly AND Carvalho, Julene AND da Silva Costa, Fabricio AND Savvidou, Makrina AND Patey, Olga AND ... | Congenital heart disease (CHD) is the most common birth defect. Many CHD phenotypes are individually rare, which limits labeled data and motivates anomaly detection for multi-modal fetal echocardiography screening. Color flow Doppler (CFD) provides critical hemodynamic cues in fetal echocardiography, yet its color over... | null | null |
Paper1304 | AnomExpert: Identifying and Selecting Anatomical Planes for Prenatal Ultrasound Anomaly Diagnosis | [
"Jian Wang",
"Yang Yang",
"Ziheng Pan",
"Xiliang Zhu",
"Yuhan Zhang",
"Yanfeng Zhou",
"Dong Ni"
] | https://papers.miccai.org/miccai-2026/0066-Paper1304.html | https://papers.miccai.org/miccai-2026/paper/1304_paper.pdf | null | null | null | [
"Body -> Fetal / Pediatric",
"Modalities -> Ultrasound",
"Applications -> Anomaly / Lesion Detection",
"Applications -> Computer-Aided Diagnosis",
"Machine Learning -> Deep Learning",
"Machine Learning -> Semi- / Weakly- / Self-supervised Learning"
] | [
"https://github.com/TIanCat/AnomExpert"
] | [] | null | @InProceedings{WanJia_AnomExpert_MICCAI2026,
author = { Wang, Jian AND Yang, Yang AND Pan, Ziheng AND Zhu, Xiliang AND Zhang, Yuhan AND Zhou, Yanfeng AND Ni, Dong},
title = { { AnomExpert: Identifying and Selecting Anatomical Planes for Prenatal Ultrasound Anomaly Diagnosis } },
booktitle = {Med... | Life-limiting congenital anomalies require accurate prenatal diagnosis for appropriate clinical decision-making. Prenatal ultrasound (US) examinations involve multiple anatomical planes, and diagnosis depends on identifying anatomical planes and selecting diagnostically relevant planes for each anomaly. Existing automa... | 2607.13409 | title_snapshot |
Paper0425 | APEX-SAM: Anatomy-Aware Prompting with Expert Retrieval for Training-Free Medical Image Segmentation | [
"Zhihao Mao",
"Bangpu Chen",
"Qi Lei",
"Jiaqi Tan",
"Kun Sun"
] | https://papers.miccai.org/miccai-2026/0067-Paper0425.html | https://papers.miccai.org/miccai-2026/paper/0425_paper.pdf | null | null | null | [
"Body -> Brain",
"Modalities -> CT / X-ray",
"Modalities -> Diffusion MRI",
"Applications -> Image Segmentation",
"Machine Learning -> Semi- / Weakly- / Self-supervised Learning"
] | [
"https://github.com/Trump0412/APEX-SAM"
] | [
"https://chaos.grand-challenge.org/",
"https://www.synapse.org/Synapse:syn3193805",
"https://zmiclab.github.io/zxh/0/mscmrseg19/"
] | null | @InProceedings{MaoZhi_APEXSAM_MICCAI2026,
author = { Mao, Zhihao AND Chen, Bangpu AND Lei, Qi AND Tan, Jiaqi AND Sun, Kun},
title = { { APEX-SAM: Anatomy-Aware Prompting with Expert Retrieval for Training-Free Medical Image Segmentation } },
booktitle = {Medical Image Computing and Computer Assi... | Training-free cross-domain few-shot medical image segmentation aims to segment unseen anatomies without parameter updates, addressing the high cost of dense annotation and domain-specific fine-tuning in clinical practice. Existing support-driven prompting methods face three limitations: support exemplars are randomly s... | null | null |
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