MICCAI
Collection
Accepted papers for MICCAI (Medical Image Computing and Computer Assisted Intervention), one dataset per year. • 4 items • Updated
paper_id string | title string | authors list | miccai_url string | pdf_url string | doi string | sharedit_url string | supplementary_url string | topics list | code_urls list | dataset_urls list | pages string | bibtex large_string | abstract large_string | arxiv_id string | arxiv_id_source string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Paper1861 | 3D Spine Shape Estimation from Single 2D DXA | [
"Emmanuelle Bourigault",
"Amir Jamaludin",
"Andrew Zisserman"
] | https://papers.miccai.org/miccai-2024/001-Paper1861.html | https://papers.miccai.org/miccai-2024/paper/1861_paper.pdf | 10.1007/978-3-031-72086-4_1 | https://rdcu.be/dV16Y | https://papers.miccai.org/miccai-2024/supp/1861_supp.zip | [
"Computer Aided Diagnosis, Treatment Response, and Outcome Prediction",
"Imaging-related Clinical Studies"
] | [
"https://github.com/EmmanuelleB985/DXA-to-3D"
] | [
"https://www.ukbiobank.ac.uk/"
] | 3-13 | @InProceedings{Bou_3D_MICCAI2024,
author = { Bourigault, Emmanuelle and Jamaludin, Amir and Zisserman, Andrew},
title = { { 3D Spine Shape Estimation from Single 2D DXA } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
year = {20... | Scoliosis is currently assessed solely on 2D lateral deviations, but recent studies have also revealed the importance of other imaging planes in understanding the deformation of the spine. Consequently, extracting the spinal geometry in 3D would help quantify these spinal deformations and aid diagnosis. In this study, ... | 2412.01504 | title_snapshot |
Paper1908 | 3D Vessel Graph Generation Using Denoising Diffusion | [
"Chinmay Prabhakar",
"Suprosanna Shit",
"Fabio Musio",
"Kaiyuan Yang",
"Tamaz Amiranashvili",
"Johannes C. Paetzold",
"Hongwei Bran Li",
"Bjoern Menze"
] | https://papers.miccai.org/miccai-2024/002-Paper1908.html | https://papers.miccai.org/miccai-2024/paper/1908_paper.pdf | 10.1007/978-3-031-72120-5_1 | https://rdcu.be/dV57W | https://papers.miccai.org/miccai-2024/supp/1908_supp.pdf | [
"Clinical applications - Vascular",
"Machine Learning - Other",
"Modalities - MRI"
] | [
"https://github.com/chinmay5/vessel_diffuse"
] | [] | 3-13 | @InProceedings{Pra_3D_MICCAI2024,
author = { Prabhakar, Chinmay and Shit, Suprosanna and Musio, Fabio and Yang, Kaiyuan and Amiranashvili, Tamaz and Paetzold, Johannes C. and Li, Hongwei Bran and Menze, Bjoern},
title = { { 3D Vessel Graph Generation Using Denoising Diffusion } },
booktitle = {p... | Blood vessel networks, represented as 3D graphs, help predict disease biomarkers, simulate blood flow, and aid in synthetic image generation, relevant in both clinical and pre-clinical settings. However, generating realistic vessel graphs that correspond to an anatomy of interest is challenging. Previous methods aimed ... | 2407.05842 | title_snapshot |
Paper1001 | 3DDX: Bone Surface Reconstruction from a Single Standard-Geometry Radiograph via Dual-Face Depth Estimation | [
"Yi Gu",
"Yoshito Otake",
"Keisuke Uemura",
"Masaki Takao",
"Mazen Soufi",
"Seiji Okada",
"Nobuhiko Sugano",
"Hugues Talbot",
"Yoshinobu Sato"
] | https://papers.miccai.org/miccai-2024/003-Paper1001.html | https://papers.miccai.org/miccai-2024/paper/1001_paper.pdf | 10.1007/978-3-031-72104-5_1 | https://rdcu.be/dV5Bv | https://papers.miccai.org/miccai-2024/supp/1001_supp.pdf | [
"Image Formation and Reconstruction",
"Modalities - CT",
"Modalities - other"
] | [
"https://github.com/Kayaba-Akihiko/3DDX"
] | [] | 3-13 | @InProceedings{Gu_3DDX_MICCAI2024,
author = { Gu, Yi and Otake, Yoshito and Uemura, Keisuke and Takao, Masaki and Soufi, Mazen and Okada, Seiji and Sugano, Nobuhiko and Talbot, Hugues and Sato, Yoshinobu},
title = { { 3DDX: Bone Surface Reconstruction from a Single Standard-Geometry Radiograph via Dual-... | Radiography is widely used in orthopedics for its affordability and low radiation exposure. 3D reconstruction from a single radiograph, so-called 2D-3D reconstruction, offers the possibility of various clinical applications, but achieving clinically viable accuracy and computational efficiency is still an unsolved chal... | 2409.16702 | title_snapshot |
Paper0132 | 3DGPS: A 3D Differentiable-Gaussian-based Planning Strategy for Liver Tumor Cryoablation | [
"Ce Wang",
"Xiaoyu Huang",
"Yaqing Kong",
"Qian Li",
"You Hao",
"Xiang Zhou"
] | https://papers.miccai.org/miccai-2024/004-Paper0132.html | https://papers.miccai.org/miccai-2024/paper/0132_paper.pdf | 10.1007/978-3-031-72089-5_1 | https://rdcu.be/dV5vR | https://papers.miccai.org/miccai-2024/supp/0132_supp.pdf | [
"Surgical Planning and Simulation",
"Image-Guided Interventions and Surgery",
"Modalities - CT"
] | [] | [] | 3-13 | @InProceedings{Wan_3DGPS_MICCAI2024,
author = { Wang, Ce and Huang, Xiaoyu and Kong, Yaqing and Li, Qian and Hao, You and Zhou, Xiang},
title = { { 3DGPS: A 3D Differentiable-Gaussian-based Planning Strategy for Liver Tumor Cryoablation } },
booktitle = {proceedings of Medical Image Computing an... | Effective preoperative planning is crucial for successful cryoablation of liver tumors. However, conventional planning methods rely heavily on clinicians’ experience, which may not always lead to an optimal solution due to the intricate 3D anatomical structures and clinical constraints. Lots of planning methods have be... | null | null |
Paper1015 | 3DGR-CAR: Coronary artery reconstruction from ultra-sparse 2D X-ray views with a 3D Gaussians representation | [
"Xueming Fu",
"Yingtai Li",
"Fenghe Tang",
"Jun Li",
"Mingyue Zhao",
"Gao-Jun Teng",
"S. Kevin Zhou"
] | https://papers.miccai.org/miccai-2024/005-Paper1015.html | https://papers.miccai.org/miccai-2024/paper/1015_paper.pdf | 10.1007/978-3-031-72104-5_2 | https://rdcu.be/dV5Bw | https://papers.miccai.org/miccai-2024/supp/1015_supp.pdf | [
"Clinical applications - Vascular",
"Image Formation and Reconstruction",
"Modalities - CT"
] | [
"https://github.com/windrise/3DGR-CAR"
] | [
"https://asoca.grand-challenge.org",
"https://github.com/XiaoweiXu/ImageCAS-A-Large-Scale-Dataset-and-Benchmark-for-Coronary-Artery-Segmentation-based-on-CT"
] | 14-24 | @InProceedings{Fu_3DGRCAR_MICCAI2024,
author = { Fu, Xueming and Li, Yingtai and Tang, Fenghe and Li, Jun and Zhao, Mingyue and Teng, Gao-Jun and Zhou, S. Kevin},
title = { { 3DGR-CAR: Coronary artery reconstruction from ultra-sparse 2D X-ray views with a 3D Gaussians representation } },
booktit... | Reconstructing 3D coronary arteries is important for coronary artery disease diagnosis, treatment planning and operation navigation. Traditional techniques often require many projections, while reconstruction from sparse-view X-ray projections is a potential way of reducing radiation dose. However, the extreme sparsity... | 2410.00404 | title_snapshot |
Paper2442 | 3DPX: Progressive 2D-to-3D Oral Image Reconstruction with Hybrid MLP-CNN Networks | [
"Xiaoshuang Li",
"Mingyuan Meng",
"Zimo Huang",
"Lei Bi",
"Eduardo Delamare",
"Dagan Feng",
"Bin Sheng",
"Jinman Kim"
] | https://papers.miccai.org/miccai-2024/006-Paper2442.html | https://papers.miccai.org/miccai-2024/paper/2442_paper.pdf | 10.1007/978-3-031-72104-5_3 | https://rdcu.be/dV5Bx | https://papers.miccai.org/miccai-2024/supp/2442_supp.pdf | [
"Image Formation and Reconstruction",
"Visualization in Biomedical Imaging"
] | [] | [] | 25-34 | @InProceedings{Li_3DPX_MICCAI2024,
author = { Li, Xiaoshuang and Meng, Mingyuan and Huang, Zimo and Bi, Lei and Delamare, Eduardo and Feng, Dagan and Sheng, Bin and Kim, Jinman},
title = { { 3DPX: Progressive 2D-to-3D Oral Image Reconstruction with Hybrid MLP-CNN Networks } },
booktitle = {proce... | Panoramic X-ray (PX) is a prevalent modality in dental practice for its wide availability and low cost. However, as a 2D projection image, PX does not contain 3D anatomical information, and therefore has limited use in dental applications that can benefit from 3D information, e.g., tooth angular misalignment detection ... | 2408.01292 | title_snapshot |
Paper2090 | 3D-SAutoMed: Automatic Segment Anything Model for 3D Medical Image Segmentation from Local-Global Perspective | [
"Junjie Liang",
"Peng Cao",
"Wenju Yang",
"Jinzhu Yang",
"Osmar R. Zaiane"
] | https://papers.miccai.org/miccai-2024/007-Paper2090.html | https://papers.miccai.org/miccai-2024/paper/2090_paper.pdf | 10.1007/978-3-031-72114-4_1 | https://rdcu.be/dV5J5 | null | [
"Image Segmentation",
"Machine Learning - Foundation Models"
] | [] | [] | 3-12 | @InProceedings{Lia_3DSAutoMed_MICCAI2024,
author = { Liang, Junjie and Cao, Peng and Yang, Wenju and Yang, Jinzhu and Zaiane, Osmar R.},
title = { { 3D-SAutoMed: Automatic Segment Anything Model for 3D Medical Image Segmentation from Local-Global Perspective } },
booktitle = {proceedings of Medi... | 3D medical image segmentation is critical for clinical diagnosis and treatment planning. Recently, with the powerful generalization, the foundational segmentation model SAM is widely used in medical images. However, the existing SAM variants still have many limitations including lack of 3D-aware ability and automatic p... | null | null |
Paper3648 | 7T MRI Synthesization from 3T Acquisitions | [
"Qiming Cui",
"Duygu Tosun",
"Pratik Mukherjee",
"Reza Abbasi-Asl"
] | https://papers.miccai.org/miccai-2024/008-Paper3648.html | https://papers.miccai.org/miccai-2024/paper/3648_paper.pdf | 10.1007/978-3-031-72104-5_4 | https://rdcu.be/dV5By | https://papers.miccai.org/miccai-2024/supp/3648_supp.pdf | [
"Machine Learning - Other",
"Clinical applications - Neuroimaging - Others",
"Modalities - MRI"
] | [
"https://github.com/abbasilab/Synthetic_7T_MRI"
] | [] | 35-44 | @InProceedings{Cui_7T_MICCAI2024,
author = { Cui, Qiming and Tosun, Duygu and Mukherjee, Pratik and Abbasi-Asl, Reza},
title = { { 7T MRI Synthesization from 3T Acquisitions } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
year ... | Supervised deep learning techniques can be used to generate synthetic 7T MRIs from 3T MRI inputs. This image enhancement process leverages the advantages of ultra-high-field MRI to improve the signal-to-noise and contrast-to-noise ratios of 3T acquisitions. In this paper, we introduce multiple novel 7T synthesization a... | 2403.08979 | title_snapshot |
Paper0219 | A Bayesian Approach to Weakly-supervised Laparoscopic Image Segmentation | [
"Zhou Zheng",
"Yuichiro Hayashi",
"Masahiro Oda",
"Takayuki Kitasaka",
"Kensaku Mori"
] | https://papers.miccai.org/miccai-2024/009-Paper0219.html | https://papers.miccai.org/miccai-2024/paper/0219_paper.pdf | 10.1007/978-3-031-72089-5_2 | https://rdcu.be/dV5vS | https://papers.miccai.org/miccai-2024/supp/0219_supp.pdf | [
"Modalities - Endoscope",
"Image Segmentation",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised Representation Learning"
] | [
"https://github.com/MoriLabNU/Bayesian_WSS"
] | [
"https://www.kaggle.com/datasets/newslab/cholecseg8k",
"https://autolaparo.github.io",
"https://www.creatis.insa-lyon.fr/Challenge/acdc",
"https://vios-s.github.io/multiscale-adversarial-attention-gates"
] | 14-24 | @InProceedings{Zhe_ABayesian_MICCAI2024,
author = { Zheng, Zhou and Hayashi, Yuichiro and Oda, Masahiro and Kitasaka, Takayuki and Mori, Kensaku},
title = { { A Bayesian Approach to Weakly-supervised Laparoscopic Image Segmentation } },
booktitle = {proceedings of Medical Image Computing and Com... | In this paper, we study weakly-supervised laparoscopic image segmentation with sparse annotations. We introduce a novel Bayesian deep learning approach designed to enhance both the accuracy and interpretability of the model’s segmentation, founded upon a comprehensive Bayesian framework, ensuring a robust and theoretic... | 2410.08509 | title_snapshot |
Paper2774 | A Clinical-oriented Lightweight Network for High-resolution Medical Image Enhancement | [
"Yaqi Wang",
"Leqi Chen",
"Qingshan Hou",
"Peng Cao",
"Jinzhu Yang",
"Xiaoli Liu",
"Osmar R. Zaiane"
] | https://papers.miccai.org/miccai-2024/010-Paper2774.html | https://papers.miccai.org/miccai-2024/paper/2774_paper.pdf | 10.1007/978-3-031-72384-1_1 | https://rdcu.be/dV1Va | https://papers.miccai.org/miccai-2024/supp/2774_supp.pdf | [
"Imaging-related Clinical Studies",
"Image Formation and Reconstruction",
"Machine Learning - Interpretability / Explainability"
] | [] | [] | 3-12 | @InProceedings{Wan_AClinicaloriented_MICCAI2024,
author = { Wang, Yaqi and Chen, Leqi and Hou, Qingshan and Cao, Peng and Yang, Jinzhu and Liu, Xiaoli and Zaiane, Osmar R.},
title = { { A Clinical-oriented Lightweight Network for High-resolution Medical Image Enhancement } },
booktitle = {procee... | Medical images captured in less-than-optimal conditions may suffer from quality degradation, such as blur, artifacts, and low lighting, which potentially leads to misdiagnosis. Unfortunately, state-of-the-art medical image enhancement methods face challenges in both high-resolution image quality enhancement and local d... | null | null |
Paper2364 | A Clinical-oriented Multi-level Contrastive Learning Method for Disease Diagnosis in Low-quality Medical Images | [
"Qingshan Hou",
"Shuai Cheng",
"Peng Cao",
"Jinzhu Yang",
"Xiaoli Liu",
"Yih Chung Tham",
"Osmar R. Zaiane"
] | https://papers.miccai.org/miccai-2024/011-Paper2364.html | https://papers.miccai.org/miccai-2024/paper/2364_paper.pdf | 10.1007/978-3-031-72384-1_2 | https://rdcu.be/dV1Vb | https://papers.miccai.org/miccai-2024/supp/2364_supp.pdf | [
"Imaging-related Clinical Studies",
"Machine Learning - Interpretability / Explainability",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised Representation Learning"
] | [] | [] | 13-23 | @InProceedings{Hou_AClinicaloriented_MICCAI2024,
author = { Hou, Qingshan and Cheng, Shuai and Cao, Peng and Yang, Jinzhu and Liu, Xiaoli and Tham, Yih Chung and Zaiane, Osmar R.},
title = { { A Clinical-oriented Multi-level Contrastive Learning Method for Disease Diagnosis in Low-quality Medical Images... | Representation learning offers a conduit to elucidate distinctive features within the latent space and interpret the deep models. However, the randomness of lesion distribution and the complexity of low-quality factors in medical images pose great challenges for models to extract key lesion features. Disease diagnosis ... | 2404.04887 | title_snapshot |
Paper2514 | A Curvature-Guided Coarse-to-Fine Framework for Enhanced Whole Brain Segmentation | [
"Fenqiang Zhao",
"Yuxing Tang",
"Le Lu",
"Ling Zhang"
] | https://papers.miccai.org/miccai-2024/012-Paper2514.html | https://papers.miccai.org/miccai-2024/paper/2514_paper.pdf | 10.1007/978-3-031-72114-4_2 | https://rdcu.be/dV5J6 | null | [
"Image Segmentation",
"Clinical applications - Neuroimaging - Others",
"Image Registration",
"Modalities - MRI"
] | [] | [] | 13-22 | @InProceedings{Zha_ACurvatureGuided_MICCAI2024,
author = { Zhao, Fenqiang and Tang, Yuxing and Lu, Le and Zhang, Ling},
title = { { A Curvature-Guided Coarse-to-Fine Framework for Enhanced Whole Brain Segmentation } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted Int... | Whole brain segmentation, which divides the entire brain volume into anatomically labeled regions of interest (ROIs), is a crucial step in brain image analysis. Traditional methods often rely on intricate pipelines that, while accurate, are time-consuming and require expertise due to their complexity. Alternatively, en... | null | null |
Paper1668 | A Deep Learning Approach for Placing Magnetic Resonance Spectroscopy Voxels in Brain Tumors | [
"Sangyoon Lee",
"Francesca Branzoli",
"Thanh Nguyen",
"Ovidiu Andronesi",
"Alexander Lin",
"Roberto Liserre",
"Gerd Melkus",
"Clark Chen",
"Małgorzata Marjańska",
"Patrick J. Bolan"
] | https://papers.miccai.org/miccai-2024/013-Paper1668.html | https://papers.miccai.org/miccai-2024/paper/1668_paper.pdf | 10.1007/978-3-031-72384-1_51 | https://rdcu.be/dV1WQ | https://papers.miccai.org/miccai-2024/supp/1668_supp.pdf | [
"MIC and CAI Solutions for Minimally Trained Healthcare Workers",
"Clinical applications - Neuroimaging - Others",
"Clinical applications - Oncology",
"Image-Guided Interventions and Surgery",
"Modalities - MRI",
"Modalities - other"
] | [] | [] | 543-552 | @InProceedings{Lee_ADeep_MICCAI2024,
author = { Lee, Sangyoon and Branzoli, Francesca and Nguyen, Thanh and Andronesi, Ovidiu and Lin, Alexander and Liserre, Roberto and Melkus, Gerd and Chen, Clark and Marjańska, Małgorzata and Bolan, Patrick J.},
title = { { A Deep Learning Approach for Placing Magnet... | Magnetic resonance spectroscopy (MRS) of brain tumors provides useful metabolic information for diagnosis, treatment response, and prognosis. Single-voxel MRS requires precise planning of the acquisition volume to produce a high-quality signal localized in the pathology of interest. Appropriate placement of the voxel i... | null | null |
Paper1802 | A Domain Adaption Approach for EEG-based Automated Seizure Classification with Temporal-Spatial-Spectral Attention | [
"Xiaoya Fan",
"Pengzhi Xu",
"Qi Zhao",
"Chenru Hao",
"Zheng Zhao",
"Zhong Wang"
] | https://papers.miccai.org/miccai-2024/014-Paper1802.html | https://papers.miccai.org/miccai-2024/paper/1802_paper.pdf | 10.1007/978-3-031-72086-4_2 | https://rdcu.be/dV161 | https://papers.miccai.org/miccai-2024/supp/1802_supp.pdf | [
"Modalities - EEG/ECG",
"Computer Aided Diagnosis, Treatment Response, and Outcome Prediction",
"Machine Learning - Attention models"
] | [
"https://github.com/Dondlut/EEG_DOMAIN"
] | [
"https://isip.piconepress.com/projects/nedc/html/tuh_eeg/"
] | 14-24 | @InProceedings{Fan_ADomain_MICCAI2024,
author = { Fan, Xiaoya and Xu, Pengzhi and Zhao, Qi and Hao, Chenru and Zhao, Zheng and Wang, Zhong},
title = { { A Domain Adaption Approach for EEG-based Automated Seizure Classification with Temporal-Spatial-Spectral Attention } },
booktitle = {proceeding... | Electroencephalography (EEG) based automated seizure classification can significantly ameliorate seizure diagnosis and treatment. However, the intra- and inter- subject variability in EEG data make it a challenging task. Especially, a model trained on data from multiple subjects typically degenerates when applied to ne... | null | null |
Paper0143 | A Foundation Model for Brain Lesion Segmentation with Mixture of Modality Experts | [
"Xinru Zhang",
"Ni Ou",
"Berke Doga Basaran",
"Marco Visentin",
"Mengyun Qiao",
"Renyang Gu",
"Cheng Ouyang",
"Yaou Liu",
"Paul M. Matthews",
"Chuyang Ye",
"Wenjia Bai"
] | https://papers.miccai.org/miccai-2024/015-Paper0143.html | https://papers.miccai.org/miccai-2024/paper/0143_paper.pdf | 10.1007/978-3-031-72390-2_36 | https://rdcu.be/dY6f5 | https://papers.miccai.org/miccai-2024/supp/0143_supp.pdf | [
"Machine Learning - Foundation Models",
"Clinical applications - Neuroimaging - Others",
"Image Segmentation",
"Machine Learning - Interpretability / Explainability",
"Machine Learning - Transfer Learning",
"Modalities - MRI"
] | [
"https://github.com/ZhangxinruBIT/MoME"
] | [
"https://fcon_1000.projects.nitrc.org/indi/retro/atlas.html",
"https://www.isles-challenge.org/",
"http://www.brainTumoursegmentation.org/",
"https://wmh.isi.uu.nl/\\#_Toc122355653",
"https://portal.fli-iam.irisa.fr/msseg-challenge/",
"https://www.oasis-brains.org/"
] | 379-389 | @InProceedings{Zha_AFoundation_MICCAI2024,
author = { Zhang, Xinru and Ou, Ni and Basaran, Berke Doga and Visentin, Marco and Qiao, Mengyun and Gu, Renyang and Ouyang, Cheng and Liu, Yaou and Matthews, Paul M. and Ye, Chuyang and Bai, Wenjia},
title = { { A Foundation Model for Brain Lesion Segmentation... | Brain lesion segmentation plays an essential role in neurological research and diagnosis. As brain lesions can be caused by various pathological alterations, different types of brain lesions tend to manifest with different characteristics on different imaging modalities. Due to this complexity, brain lesion segmentatio... | 2405.10246 | title_snapshot |
Paper1535 | A framework for assessing joint human-AI systems based on uncertainty estimation | [
"Emir Konuk",
"Robert Welch",
"Filip Christiansen",
"Elisabeth Epstein",
"Kevin Smith"
] | https://papers.miccai.org/miccai-2024/016-Paper1535.html | https://papers.miccai.org/miccai-2024/paper/1535_paper.pdf | 10.1007/978-3-031-72117-5_1 | https://rdcu.be/dV53F | https://papers.miccai.org/miccai-2024/supp/1535_supp.pdf | [
"Machine Learning - Uncertainty",
"Clinical applications - Oncology",
"Computer Aided Diagnosis, Treatment Response, and Outcome Prediction"
] | [] | [] | 3-12 | @InProceedings{Kon_Aframework_MICCAI2024,
author = { Konuk, Emir and Welch, Robert and Christiansen, Filip and Epstein, Elisabeth and Smith, Kevin},
title = { { A framework for assessing joint human-AI systems based on uncertainty estimation } },
booktitle = {proceedings of Medical Image Computi... | We investigate the role of uncertainty quantification in aiding medical decision-making. Existing evaluation metrics fail to capture the practical utility of joint human-AI decision-making systems. To address this, we introduce a novel framework to assess such systems and use it to benchmark a diverse set of confidence... | null | null |
Paper1165 | A Graph-Embedded Latent Space Learning and Clustering Framework for Incomplete Multimodal Multiclass Alzheimer’s Disease Diagnosis | [
"Zaixin Ou",
"Caiwen Jiang",
"Yuxiao Liu",
"Yuanwang Zhang",
"Zhiming Cui",
"Dinggang Shen"
] | https://papers.miccai.org/miccai-2024/017-Paper1165.html | https://papers.miccai.org/miccai-2024/paper/1165_paper.pdf | 10.1007/978-3-031-72104-5_5 | https://rdcu.be/dV5Bz | https://papers.miccai.org/miccai-2024/supp/1165_supp.pdf | [
"Image Formation and Reconstruction",
"Clinical applications - Neuroimaging - Others",
"Modalities - MRI",
"Modalities - PET/SPECT"
] | [
"https://github.com/Ouzaixin/Graph-SLC"
] | [
"https://adni.loni.usc.edu/data-samples/access-data/"
] | 45-55 | @InProceedings{Ou_AGraphEmbedded_MICCAI2024,
author = { Ou, Zaixin and Jiang, Caiwen and Liu, Yuxiao and Zhang, Yuanwang and Cui, Zhiming and Shen, Dinggang},
title = { { A Graph-Embedded Latent Space Learning and Clustering Framework for Incomplete Multimodal Multiclass Alzheimer’s Disease Diagnosis } ... | Alzheimer’s disease (AD) is an irreversible neurodegenerative disease, where early diagnosis is crucial for improving prognosis and delaying the progression of the disease. Leveraging multimodal PET images, which can reflect various biomarkers like Aβ and tau protein, is a promising method for AD diagnosis. However, du... | null | null |
Paper1214 | A Hybrid CNN-Transformer Feature Pyramid Network for Granular Abdominal Aortic Calcification Detection from DXA Images | [
"Zaid Ilyas",
"Afsah Saleem",
"David Suter",
"John T. Schousboe",
"William D. Leslie",
"Joshua R. Lewis",
"Syed Zulqarnain Gilani"
] | https://papers.miccai.org/miccai-2024/018-Paper1214.html | https://papers.miccai.org/miccai-2024/paper/1214_paper.pdf | 10.1007/978-3-031-72120-5_2 | https://rdcu.be/dV57X | https://papers.miccai.org/miccai-2024/supp/1214_supp.pdf | [
"Machine Learning - Attention models",
"Modalities - other"
] | [
"https://github.com/zaidilyas89/Hybrid-FPN-AACNet"
] | [] | 14-25 | @InProceedings{Ily_AHybrid_MICCAI2024,
author = { Ilyas, Zaid and Saleem, Afsah and Suter, David and Schousboe, John T. and Leslie, William D. and Lewis, Joshua R. and Gilani, Syed Zulqarnain},
title = { { A Hybrid CNN-Transformer Feature Pyramid Network for Granular Abdominal Aortic Calcification Detec... | Cardiovascular Diseases (CVDs) stand as the primary global cause of mortality, with Abdominal Aortic Calcification (AAC) being a stable marker of these conditions. AAC can be observed in Dual Energy X-ray absorptiometry (DXA) lateral view Vertebral Fracture Assessment (VFA) scans, usually performed for the detection of... | null | null |
Paper4063 | A Hyperreflective Foci Segmentation Network for OCT Images with Multi-dimensional Semantic Enhancement | [
"Xingguo Wang",
"Yuhui Ma",
"Xinyu Guo",
"Yalin Zheng",
"Jiong Zhang",
"Yonghuai Liu",
"Yitian Zhao"
] | https://papers.miccai.org/miccai-2024/019-Paper4063.html | https://papers.miccai.org/miccai-2024/paper/4063_paper.pdf | 10.1007/978-3-031-72378-0_60 | https://rdcu.be/dVZiZ | null | [
"Clinical applications - Ophthalmology",
"Image Segmentation",
"Machine Learning - Attention models",
"Modalities - other",
"Visualization in Biomedical Imaging"
] | [
"https://github.com/iMED-Lab/MUSEnet-Pytorch"
] | [
"https://github.com/iMED-Lab/MUSEnet-Pytorch"
] | 645-655 | @InProceedings{Wan_AHyperreflective_MICCAI2024,
author = { Wang, Xingguo and Ma, Yuhui and Guo, Xinyu and Zheng, Yalin and Zhang, Jiong and Liu, Yonghuai and Zhao, Yitian},
title = { { A Hyperreflective Foci Segmentation Network for OCT Images with Multi-dimensional Semantic Enhancement } },
boo... | Diabetic macular edema (DME) is a leading cause of vision loss worldwide. Optical Coherence Tomography (OCT) serves as a widely accepted imaging tool for diagnosing DME due to its non-invasiveness and high resolution cross-sectional view. Clinical evaluation of Hyperreflective Foci (HRF) in OCT contributes to understan... | null | null |
Paper4180 | A Large-scale Multi Domain Leukemia Dataset for the White Blood Cells Detection with Morphological Attributes for Explainability | [
"Abdul Rehman",
"Talha Meraj",
"Aiman Mahmood Minhas",
"Ayisha Imran",
"Mohsen Ali",
"Waqas Sultani"
] | https://papers.miccai.org/miccai-2024/020-Paper4180.html | https://papers.miccai.org/miccai-2024/paper/4180_paper.pdf | 10.1007/978-3-031-72384-1_52 | https://rdcu.be/dV1WR | https://papers.miccai.org/miccai-2024/supp/4180_supp.pdf | [
"MIC and CAI Solutions for Remote and Rural Regions",
"Human-centred AI in Medical Imaging",
"Low-cost and Point-of-care Imaging Solutions",
"Machine Learning - Transfer Learning"
] | [
"https://github.com/intelligentMachines-ITU/Blood-Cancer-Dataset"
] | [
"https://github.com/intelligentMachines-ITU/Blood-Cancer-Dataset"
] | 553-563 | @InProceedings{Reh_ALargescale_MICCAI2024,
author = { Rehman, Abdul and Meraj, Talha and Minhas, Aiman Mahmood and Imran, Ayisha and Ali, Mohsen and Sultani, Waqas},
title = { { A Large-scale Multi Domain Leukemia Dataset for the White Blood Cells Detection with Morphological Attributes for Explainabili... | Earlier diagnosis of Leukemia can save thousands of lives annually. The prognosis of leukemia is challenging without the morphological information of White Blood Cells (WBC) and relies on the accessibility of expensive microscopes and the availability of hematologists to analyze Peripheral Blood Samples (PBS). Deep Lea... | 2405.10803 | title_snapshot |
Paper0148 | A Multi-Information Dual-Layer Cross-Attention Model for Esophageal Fistula Prognosis | [
"Jianqiao Zhang",
"Hao Xiong",
"Qiangguo Jin",
"Tian Feng",
"Jiquan Ma",
"Ping Xuan",
"Peng Cheng",
"Zhiyuan Ning",
"Zhiyu Ning",
"Changyang Li",
"Linlin Wang",
"Hui Cui"
] | https://papers.miccai.org/miccai-2024/021-Paper0148.html | https://papers.miccai.org/miccai-2024/paper/0148_paper.pdf | 10.1007/978-3-031-72086-4_3 | https://rdcu.be/dV162 | null | [
"Computer Aided Diagnosis, Treatment Response, and Outcome Prediction",
"Integration of Imaging with Non-Imaging Biomarkers",
"Modalities - CT"
] | [] | [] | 25-35 | @InProceedings{Zha_AMultiInformation_MICCAI2024,
author = { Zhang, Jianqiao and Xiong, Hao and Jin, Qiangguo and Feng, Tian and Ma, Jiquan and Xuan, Ping and Cheng, Peng and Ning, Zhiyuan and Ning, Zhiyu and Li, Changyang and Wang, Linlin and Cui, Hui},
title = { { A Multi-Information Dual-Layer Cross-A... | Esophageal fistula (EF) is a critical and life-threatening complication following radiotherapy treatment for esophageal cancer (EC). Albeit tabular clinical data contains other clinically valuable information, it is inherently different from CT images and the heterogeneity among them may impede the effective fusion of ... | null | null |
Paper1213 | A New Benchmark In Vivo Paired Dataset for Laparoscopic Image De-smoking | [
"Wenyao Xia",
"Victoria Fan",
"Terry Peters",
"Elvis C. S. Chen"
] | https://papers.miccai.org/miccai-2024/022-Paper1213.html | https://papers.miccai.org/miccai-2024/paper/1213_paper.pdf | 10.1007/978-3-031-72378-0_1 | https://rdcu.be/dVY6s | null | [
"Clinical applications - Abdomen",
"Machine Learning - Other"
] | [] | [
"https://github.com/wxia43/DesmokeData"
] | 3-13 | @InProceedings{Xia_ANew_MICCAI2024,
author = { Xia, Wenyao and Fan, Victoria and Peters, Terry and Chen, Elvis C. S.},
title = { { A New Benchmark In Vivo Paired Dataset for Laparoscopic Image De-smoking } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention ... | The single greatest obstacle in developing effective algorithms for removing surgical smoke in laparoscopic surgery is the lack of a paired dataset featuring real smoky and smoke-free surgical scenes. Consequently, existing de-smoking algorithms are developed and evaluated based on atmospheric scattering models, synthe... | null | null |
Paper2715 | A New Cine-MRI Segmentation Method of Tongue Dorsum for Postoperative Swallowing Function Analysis | [
"Minghao Sun",
"Tian Zhou",
"Chenghui Jiang",
"Xiaodan Lv",
"Han Yu"
] | https://papers.miccai.org/miccai-2024/023-Paper2715.html | https://papers.miccai.org/miccai-2024/paper/2715_paper.pdf | 10.1007/978-3-031-72384-1_3 | https://rdcu.be/dV1Vc | null | [
"Imaging-related Clinical Studies",
"Computer Aided Diagnosis, Treatment Response, and Outcome Prediction",
"Image Segmentation",
"Image-Guided Interventions and Surgery",
"Machine Learning - Attention models",
"Machine Learning - Data Efficient Learning",
"Machine Learning - Other",
"MIC and CAI for ... | [
"https://github.com/MinghaoSam/SwallowingFunctionAnalysis"
] | [
"https://github.com/MinghaoSam/SwallowingFunctionAnalysis"
] | 24-34 | @InProceedings{Sun_ANew_MICCAI2024,
author = { Sun, Minghao and Zhou, Tian and Jiang, Chenghui and Lv, Xiaodan and Yu, Han},
title = { { A New Cine-MRI Segmentation Method of Tongue Dorsum for Postoperative Swallowing Function Analysis } },
booktitle = {proceedings of Medical Image Computing and... | Advantages of cine-MRI include high spatial-temporal resolution and free radia-tion, and the technique has become a new method for analyzing and assessing the swallowing function of patients with head and neck tumors. To reduce the labor work of physicians and improve the robustness of labeling the cine-MRI images, we ... | null | null |
Paper4098 | A New Dataset and Baseline Model for Rectal Cancer Risk Assessment in Endoscopic Ultrasound Videos | [
"Jiansong Zhang",
"Shengnan Wu",
"Peizhong Liu",
"Linlin Shen"
] | https://papers.miccai.org/miccai-2024/024-Paper4098.html | https://papers.miccai.org/miccai-2024/paper/4098_paper.pdf | 10.1007/978-3-031-72384-1_53 | https://rdcu.be/dV1WS | null | [
"Biomedical Image Computing for Neglected Diseases",
"Clinical applications - Oncology",
"Computer Aided Diagnosis, Treatment Response, and Outcome Prediction",
"Machine Learning - Attention models",
"Machine Learning - Foundation Models",
"Modalities - Ultrasound",
"Modalities - Video"
] | [] | [] | 564-573 | @InProceedings{Zha_ANew_MICCAI2024,
author = { Zhang, Jiansong and Wu, Shengnan and Liu, Peizhong and Shen, Linlin},
title = { { A New Dataset and Baseline Model for Rectal Cancer Risk Assessment in Endoscopic Ultrasound Videos } },
booktitle = {proceedings of Medical Image Computing and Compute... | Early diagnosis of rectal cancer is essential to improve patient survival. Existing diagnostic methods mainly rely on complex MRI as well as pathology-level co-diagnosis. In contrast, in this paper, we collect and annotate for the first time a rectal cancer ultrasound en- doscopy video dataset containing 207 patients f... | null | null |
Paper3658 | A New Non-Invasive AI-Based Diagnostic System for Automated Diagnosis of Acute Renal Rejection in Kidney Transplantation: Analysis of ADC Maps Extracted from Matched 3D Iso-Regions of the Transplanted Kidney | [
"Ibrahim Abdelhalim",
"Mohamed Abou El-Ghar",
"Amy Dwyer",
"Rosemary Ouseph",
"Sohail Contractor",
"Ayman El-Baz"
] | https://papers.miccai.org/miccai-2024/025-Paper3658.html | https://papers.miccai.org/miccai-2024/paper/3658_paper.pdf | 10.1007/978-3-031-72390-2_37 | https://rdcu.be/dY6f6 | null | [
"Machine Learning - Foundation Models",
"Clinical applications - Abdomen",
"Computer Aided Diagnosis, Treatment Response, and Outcome Prediction",
"Machine Learning - Attention models",
"Modalities - MRI"
] | [] | [] | 390-398 | @InProceedings{Abd_ANew_MICCAI2024,
author = { Abdelhalim, Ibrahim and Abou El-Ghar, Mohamed and Dwyer, Amy and Ouseph, Rosemary and Contractor, Sohail and El-Baz, Ayman},
title = { { A New Non-Invasive AI-Based Diagnostic System for Automated Diagnosis of Acute Renal Rejection in Kidney Transplantation... | Acute allograft rejection poses a significant challenge in kidney transplantation, the primary remedy for end-stage renal disease. Timely detection is crucial for intervention and graft preservation. A notable obstacle involves ensuring consistency across Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) scanning ... | null | null |
Paper1953 | A New Perspective to Boost Performance Fairness For Medical Federated Learning | [
"Yunlu Yan",
"Lei Zhu",
"Yuexiang Li",
"Xinxing Xu",
"Rick Siow Mong Goh",
"Yong Liu",
"Salman Khan",
"Chun-Mei Feng"
] | https://papers.miccai.org/miccai-2024/026-Paper1953.html | https://papers.miccai.org/miccai-2024/paper/1953_paper.pdf | 10.1007/978-3-031-72117-5_2 | https://rdcu.be/dV53G | null | [
"Machine Learning - Model Generalizability / Federated Learning"
] | [
"https://github.com/IAMJackYan/Fed-LWR"
] | [
"https://liuquande.github.io/SAML/",
"https://github.com/emma-sjwang/Dofe"
] | 13-23 | @InProceedings{Yan_ANew_MICCAI2024,
author = { Yan, Yunlu and Zhu, Lei and Li, Yuexiang and Xu, Xinxing and Goh, Rick Siow Mong and Liu, Yong and Khan, Salman and Feng, Chun-Mei},
title = { { A New Perspective to Boost Performance Fairness For Medical Federated Learning } },
booktitle = {proceed... | Improving the fairness of federated learning (FL) benefits healthy and sustainable collaboration, especially for medical applications. However, existing fair FL methods ignore the specific characteristics of medical FL applications, i.e., domain shift among the datasets from different hospitals. In this work, we propos... | 2410.19765 | title_snapshot |
Paper2689 | A Novel Adaptive Hypergraph Neural Network for Enhancing Medical Image Segmentation | [
"Shurong Chai",
"Rahul K. Jain",
"Shaocong Mo",
"Jiaqing Liu",
"Yulin Yang",
"Yinhao Li",
"Tomoko Tateyama",
"Lanfen Lin",
"Yen-Wei Chen"
] | https://papers.miccai.org/miccai-2024/027-Paper2689.html | https://papers.miccai.org/miccai-2024/paper/2689_paper.pdf | 10.1007/978-3-031-72114-4_3 | https://rdcu.be/dV5J7 | https://papers.miccai.org/miccai-2024/supp/2689_supp.pdf | [
"Image Segmentation",
"Modalities - CT"
] | [
"https://github.com/11yxk/AHGNN"
] | [] | 23-33 | @InProceedings{Cha_ANovel_MICCAI2024,
author = { Chai, Shurong and Jain, Rahul K. and Mo, Shaocong and Liu, Jiaqing and Yang, Yulin and Li, Yinhao and Tateyama, Tomoko and Lin, Lanfen and Chen, Yen-Wei},
title = { { A Novel Adaptive Hypergraph Neural Network for Enhancing Medical Image Segmentation } },... | Medical image segmentation is crucial in the field of medical imaging, assisting healthcare professionals in analyzing images and improving diagnostic performance. Recent advancements in Transformer-based networks, which utilize self-attention mechanism, have proven their effectiveness in various medical problems, incl... | null | null |
Paper2053 | A Novel Tracking Framework for Devices in X-ray Leveraging Supplementary Cue-Driven Self-Supervised Features | [
"Saahil Islam",
"Venkatesh N. Murthy",
"Dominik Neumann",
"Serkan Cimen",
"Puneet Sharma",
"Andreas Maier",
"Dorin Comaniciu",
"Florin C. Ghesu"
] | https://papers.miccai.org/miccai-2024/028-Paper2053.html | https://papers.miccai.org/miccai-2024/paper/2053_paper.pdf | 10.1007/978-3-031-72089-5_3 | https://rdcu.be/dV5vT | https://papers.miccai.org/miccai-2024/supp/2053_supp.pdf | [
"Image-Guided Interventions and Surgery",
"Interventional Imaging Systems",
"Machine Learning - Attention models",
"Machine Learning - Foundation Models",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised Representation Learning",
"Modalities - Video"
] | [] | [] | 25-34 | @InProceedings{Isl_ANovel_MICCAI2024,
author = { Islam, Saahil and Murthy, Venkatesh N. and Neumann, Dominik and Cimen, Serkan and Sharma, Puneet and Maier, Andreas and Comaniciu, Dorin and Ghesu, Florin C.},
title = { { A Novel Tracking Framework for Devices in X-ray Leveraging Supplementary Cue-Driven... | To restore proper blood flow in blocked coronary arteries via angioplasty procedure, accurate placement of devices such as catheters, balloons, and stents under live Fluoroscopy or diagnostic Angiography is crucial. Identified Balloon markers help in enhancing stent visibility in X-ray sequences, while the Catheter tip... | 2501.12958 | title_snapshot |
Paper3962 | A Patient-Specific Framework for Autonomous Spinal Fixation via a Steerable Drilling Robot | [
"Susheela Sharma",
"Sarah Go",
"Zeynep Yakay",
"Yash Kulkarni",
"Siddhartha Kapuria",
"Jordan P. Amadio",
"Reza Rajebi",
"Mohsen Khadem",
"Nassir Navab",
"Farshid Alambeigi"
] | https://papers.miccai.org/miccai-2024/029-Paper3962.html | https://papers.miccai.org/miccai-2024/paper/3962_paper.pdf | 10.1007/978-3-031-72089-5_4 | https://rdcu.be/dV5vU | null | [
"Medical Robotics and Haptics",
"Image-Guided Interventions and Surgery",
"Surgical Planning and Simulation"
] | [] | [] | 35-45 | @InProceedings{Sha_APatientSpecific_MICCAI2024,
author = { Sharma, Susheela and Go, Sarah and Yakay, Zeynep and Kulkarni, Yash and Kapuria, Siddhartha and Amadio, Jordan P. and Rajebi, Reza and Khadem, Mohsen and Navab, Nassir and Alambeigi, Farshid},
title = { { A Patient-Specific Framework for Autonom... | In this paper, with the goal of enhancing the minimally invasive spinal fixation procedure in osteoporotic patients, we propose a first-of-its-kind image-guided robotic framework for performing and autonomous and patient-specific procedure using a unique concentric tube steerable drilling robot (CT-SDR). Particularly, ... | 2405.17606 | title_snapshot |
Paper1279 | A Refer-and-Ground Multimodal Large Language Model for Biomedicine | [
"Xiaoshuang Huang",
"Haifeng Huang",
"Lingdong Shen",
"Yehui Yang",
"Fangxin Shang",
"Junwei Liu",
"Jia Liu"
] | https://papers.miccai.org/miccai-2024/030-Paper1279.html | https://papers.miccai.org/miccai-2024/paper/1279_paper.pdf | 10.1007/978-3-031-72390-2_38 | https://rdcu.be/dY6f7 | https://papers.miccai.org/miccai-2024/supp/1279_supp.pdf | [
"Machine Learning - Foundation Models",
"Modalities - Integration of Imaging and Non-imaging Data"
] | [
"https://github.com/ShawnHuang497/BiRD"
] | [] | 399-409 | @InProceedings{Hua_AReferandGround_MICCAI2024,
author = { Huang, Xiaoshuang and Huang, Haifeng and Shen, Lingdong and Yang, Yehui and Shang, Fangxin and Liu, Junwei and Liu, Jia},
title = { { A Refer-and-Ground Multimodal Large Language Model for Biomedicine } },
booktitle = {proceedings of Medi... | With the rapid development of multimodal large language models (MLLMs), especially their capabilities in visual chat through refer and ground functionalities, their significance is increasingly recognized. However, the biomedical field currently exhibits a substantial gap in this area, primarily due to the absence of a... | 2406.18146 | title_snapshot |
Paper3756 | A Region-Based Approach to Diabetic Retinopathy Classification with Superpixel Tokenization | [
"Clément Playout",
"Zacharie Legault",
"Renaud Duval",
"Marie Carole Boucher",
"Farida Cheriet"
] | https://papers.miccai.org/miccai-2024/031-Paper3756.html | https://papers.miccai.org/miccai-2024/paper/3756_paper.pdf | 10.1007/978-3-031-72086-4_4 | https://rdcu.be/dV163 | https://papers.miccai.org/miccai-2024/supp/3756_supp.pdf | [
"Computer Aided Diagnosis, Treatment Response, and Outcome Prediction",
"Clinical applications - Ophthalmology",
"Machine Learning - Attention models",
"Machine Learning - Interpretability / Explainability"
] | [
"https://github.com/ClementPla/RetinalViT/tree/prototype_superpixels"
] | [
"https://www.kaggle.com/c/diabetic-retinopathy-detection/data",
"https://www.kaggle.com/c/aptos2019-blindness-detection/data",
"https://github.com/nkicsl/DDR-dataset",
"https://ieee-dataport.org/open-access/indian-diabetic-retinopathy-image-dataset-idrid"
] | 36-45 | @InProceedings{Pla_ARegionBased_MICCAI2024,
author = { Playout, Clément and Legault, Zacharie and Duval, Renaud and Boucher, Marie Carole and Cheriet, Farida},
title = { { A Region-Based Approach to Diabetic Retinopathy Classification with Superpixel Tokenization } },
booktitle = {proceedings o... | We explore the efficacy of a region-based method for image tokenization, aimed at enhancing the resolution of images fed to a Transformer. This method involves segmenting the image into regions using SLIC superpixels. Spatial features, derived from a pretrained model are aggregated segment-wise and input into a streaml... | null | null |
Paper1108 | A Scanning Laser Ophthalmoscopy Image Database and Trustworthy Retinal Disease Detection Method | [
"Yichen Hu",
"Chao Wang",
"Weitao Song",
"Aleksei Tiulpin",
"Qing Liu"
] | https://papers.miccai.org/miccai-2024/032-Paper1108.html | https://papers.miccai.org/miccai-2024/paper/1108_paper.pdf | 10.1007/978-3-031-72086-4_5 | https://rdcu.be/dV164 | https://papers.miccai.org/miccai-2024/supp/1108_supp.zip | [
"Computer Aided Diagnosis, Treatment Response, and Outcome Prediction"
] | [
"https://drive.google.com/drive/folders/1wzoCppWgUhM_9pN1kVrhmHycc8MYEj06"
] | [
"https://drive.google.com/drive/folders/1wzoCppWgUhM_9pN1kVrhmHycc8MYEj06"
] | 46-56 | @InProceedings{Hu_AScanning_MICCAI2024,
author = { Hu, Yichen and Wang, Chao and Song, Weitao and Tiulpin, Aleksei and Liu, Qing},
title = { { A Scanning Laser Ophthalmoscopy Image Database and Trustworthy Retinal Disease Detection Method } },
booktitle = {proceedings of Medical Image Computing ... | Scanning laser ophthalmoscopy (SLO) images provide ophthalmologists with a non-invasive way to examine the retina for diagnostic and treatment purposes. Manual reading SLO images by ophthalmologists is a tedious task. Thus, developing trustworthy disease detection algorithms becomes urgent. However, up to now, there ar... | null | null |
Paper2509 | A task-conditional mixture-of-experts model for missing modality segmentation | [
"Philip Novosad",
"Richard A. D. Carano",
"Anitha Priya Krishnan"
] | https://papers.miccai.org/miccai-2024/033-Paper2509.html | https://papers.miccai.org/miccai-2024/paper/2509_paper.pdf | 10.1007/978-3-031-72114-4_4 | https://rdcu.be/dV5J8 | https://papers.miccai.org/miccai-2024/supp/2509_supp.pdf | [
"Image Segmentation",
"Clinical applications - Neuroimaging - Others",
"Machine Learning - Other",
"Modalities - MRI"
] | [] | [] | 34-43 | @InProceedings{Nov_Ataskconditional_MICCAI2024,
author = { Novosad, Philip and Carano, Richard A. D. and Krishnan, Anitha Priya},
title = { { A task-conditional mixture-of-experts model for missing modality segmentation } },
booktitle = {proceedings of Medical Image Computing and Computer Assist... | Accurate quantification of multiple sclerosis (MS) lesions using multi-contrast magnetic resonance imaging (MRI) plays a crucial role in disease assessment. While many methods for automatic MS lesion segmentation in MRI are available, these methods typically require a fixed set of MRI modalities as inputs. Such full mu... | null | null |
Paper2390 | A Unified Model for Longitudinal Multi-Modal Multi-View Prediction with Missingness | [
"Boqi Chen",
"Junier Oliva",
"Marc Niethammer"
] | https://papers.miccai.org/miccai-2024/034-Paper2390.html | https://papers.miccai.org/miccai-2024/paper/2390_paper.pdf | 10.1007/978-3-031-72390-2_39 | https://rdcu.be/dY6f8 | https://papers.miccai.org/miccai-2024/supp/2390_supp.pdf | [
"Machine Learning - Foundation Models",
"Computer Aided Diagnosis, Treatment Response, and Outcome Prediction",
"Integration of Imaging with Non-Imaging Biomarkers",
"Machine Learning - Attention models",
"Machine Learning - Interpretability / Explainability",
"Modalities - Integration of Imaging and Non-... | [
"https://github.com/uncbiag/UniLMMV"
] | [
"https://nda.nih.gov/oai"
] | 410-420 | @InProceedings{Che_AUnified_MICCAI2024,
author = { Chen, Boqi and Oliva, Junier and Niethammer, Marc},
title = { { A Unified Model for Longitudinal Multi-Modal Multi-View Prediction with Missingness } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MI... | Medical records often consist of different modalities, such as images, text, and tabular information. Integrating all modalities offers a holistic view of a patient’s condition, while analyzing them longitudinally provides a better understanding of disease progression. However, real-world longitudinal medical records p... | 2403.12211 | title_snapshot |
Paper1005 | A Universal and Flexible Framework for Unsupervised Statistical Shape Model Learning | [
"Nafie El Amrani",
"Dongliang Cao",
"Florian Bernard"
] | https://papers.miccai.org/miccai-2024/035-Paper1005.html | https://papers.miccai.org/miccai-2024/paper/1005_paper.pdf | 10.1007/978-3-031-72120-5_3 | https://rdcu.be/dV57Y | https://papers.miccai.org/miccai-2024/supp/1005_supp.pdf | [
"Machine Learning - Other",
"Computational Anatomy and Physiology"
] | [
"https://github.com/NafieAmrani/FUSS"
] | [
"http://medicaldecathlon.com/",
"https://luna16.grand-challenge.org/Data/"
] | 26-36 | @InProceedings{El_AUniversal_MICCAI2024,
author = { El Amrani, Nafie and Cao, Dongliang and Bernard, Florian},
title = { { A Universal and Flexible Framework for Unsupervised Statistical Shape Model Learning } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervent... | We introduce a novel unsupervised deep learning framework for constructing statistical shape models (SSMs). Although unsupervised learning-based 3D shape matching methods have made a major leap forward in recent years, the correspondence quality of existing methods does not meet the demanding requirements necessary for... | null | null |
Paper2322 | A Wasserstein Recipe for Replicable Machine Learning on Functional Neuroimages | [
"Jiaqi Ding",
"Tingting Dan",
"Ziquan Wei",
"Paul Laurienti",
"Guorong Wu"
] | https://papers.miccai.org/miccai-2024/036-Paper2322.html | https://papers.miccai.org/miccai-2024/paper/2322_paper.pdf | 10.1007/978-3-031-72069-7_1 | https://rdcu.be/dV1Mk | null | [
"Clinical applications - Neuroimaging - Functional Brain Networks",
"Modalities - MRI"
] | [] | [] | 3-13 | @InProceedings{Din_AWasserstein_MICCAI2024,
author = { Ding, Jiaqi and Dan, Tingting and Wei, Ziquan and Laurienti, Paul and Wu, Guorong},
title = { { A Wasserstein Recipe for Replicable Machine Learning on Functional Neuroimages } },
booktitle = {proceedings of Medical Image Computing and Compu... | Advances in neuroimaging have dramatically expanded our ability to probe the neurobiological bases of behavior in-vivo. Leveraging a growing repository of publicly available neuroimaging data, there is a surging interest for utilizing machine learning approaches to explore new questions in neuroscience. Despite the imp... | null | null |
Paper1747 | A Weakly-supervised Multi-lesion Segmentation Framework Based on Target-level Incomplete Annotations | [
"Jianguo Ju",
"Shumin Ren",
"Dandan Qiu",
"Huijuan Tu",
"Juanjuan Yin",
"Pengfei Xu",
"Ziyu Guan"
] | https://papers.miccai.org/miccai-2024/037-Paper1747.html | https://papers.miccai.org/miccai-2024/paper/1747_paper.pdf | 10.1007/978-3-031-72114-4_5 | https://rdcu.be/dV5J9 | https://papers.miccai.org/miccai-2024/supp/1747_supp.pdf | [
"Image Segmentation",
"Clinical applications - Abdomen",
"Computer Aided Diagnosis, Treatment Response, and Outcome Prediction",
"Machine Learning - Foundation Models",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised Representation Learning",
"Modalities - CT"
] | [
"https://github.com/HeyJGJu/CD_TIA"
] | [] | 44-53 | @InProceedings{Ju_AWeaklysupervised_MICCAI2024,
author = { Ju, Jianguo and Ren, Shumin and Qiu, Dandan and Tu, Huijuan and Yin, Juanjuan and Xu, Pengfei and Guan, Ziyu},
title = { { A Weakly-supervised Multi-lesion Segmentation Framework Based on Target-level Incomplete Annotations } },
booktitl... | Effectively segmenting Crohn’s disease (CD) from computed tomography is crucial for clinical use. Given the difficulty of obtaining manual annotations, more and more researchers have begun to pay attention to weakly supervised methods. However, due to the challenges of designing weakly supervised frameworks with limite... | null | null |
Paper1674 | ABP: Asymmetric Bilateral Prompting for Text-guided Medical Image Segmentation | [
"Xinyi Zeng",
"Pinxian Zeng",
"Jiaqi Cui",
"Aibing Li",
"Bo Liu",
"Chengdi Wang",
"Yan Wang"
] | https://papers.miccai.org/miccai-2024/038-Paper1674.html | https://papers.miccai.org/miccai-2024/paper/1674_paper.pdf | 10.1007/978-3-031-72114-4_6 | https://rdcu.be/dV5Ka | null | [
"Image Segmentation",
"Modalities - Integration of Imaging and Non-imaging Data"
] | [] | [
"https://www.kaggle.com/datasets/aysendegerli/qatacov19-dataset"
] | 54-64 | @InProceedings{Zen_ABP_MICCAI2024,
author = { Zeng, Xinyi and Zeng, Pinxian and Cui, Jiaqi and Li, Aibing and Liu, Bo and Wang, Chengdi and Wang, Yan},
title = { { ABP: Asymmetric Bilateral Prompting for Text-guided Medical Image Segmentation } },
booktitle = {proceedings of Medical Image Comput... | Deep learning-based segmentation models have made remarkable progress in aiding pulmonary disease diagnosis by segmenting lung lesion areas in large amounts of annotated X-ray images. Recently, to alleviate the demand for medical image data and further improve segmentation performance, various studies have extended mon... | null | null |
Paper2570 | Accelerated Multi-Contrast MRI Reconstruction via Frequency and Spatial Mutual Learning | [
"Qi Chen",
"Xiaohan Xing",
"Zhen Chen",
"Zhiwei Xiong"
] | https://papers.miccai.org/miccai-2024/039-Paper2570.html | https://papers.miccai.org/miccai-2024/paper/2570_paper.pdf | 10.1007/978-3-031-72104-5_6 | https://rdcu.be/dV5BA | https://papers.miccai.org/miccai-2024/supp/2570_supp.pdf | [
"Image Formation and Reconstruction",
"Modalities - MRI"
] | [] | [] | 56-66 | @InProceedings{Che_Accelerated_MICCAI2024,
author = { Chen, Qi and Xing, Xiaohan and Chen, Zhen and Xiong, Zhiwei},
title = { { Accelerated Multi-Contrast MRI Reconstruction via Frequency and Spatial Mutual Learning } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted I... | To accelerate Magnetic Resonance (MR) imaging procedures, Multi-Contrast MR Reconstruction (MCMR) has become a prevalent trend that utilizes an easily obtainable modality as an auxiliary to support high-quality reconstruction of the target modality with under-sampled k-space measurements. The exploration of global depe... | 2409.14113 | title_snapshot |
Paper2133 | Achieving Fairness Through Channel Pruning for Dermatological Disease Diagnosis | [
"Qingpeng Kong",
"Ching-Hao Chiu",
"Dewen Zeng",
"Yu-Jen Chen",
"Tsung-Yi Ho",
"Jingtong Hu",
"Yiyu Shi"
] | https://papers.miccai.org/miccai-2024/040-Paper2133.html | https://papers.miccai.org/miccai-2024/paper/2133_paper.pdf | 10.1007/978-3-031-72117-5_3 | https://rdcu.be/dV53H | https://papers.miccai.org/miccai-2024/supp/2133_supp.pdf | [
"Machine Learning - Algorithmic Fairness"
] | [
"https://github.com/Kqp1227/Sensitive-Channel-Pruning"
] | [] | 24-34 | @InProceedings{Kon_Achieving_MICCAI2024,
author = { Kong, Qingpeng and Chiu, Ching-Hao and Zeng, Dewen and Chen, Yu-Jen and Ho, Tsung-Yi and Hu, Jingtong and Shi, Yiyu},
title = { { Achieving Fairness Through Channel Pruning for Dermatological Disease Diagnosis } },
booktitle = {proceedings of M... | Numerous studies have revealed that deep learning-based medical image classification models may exhibit bias towards specific demographic attributes, such as race, gender, and age. Existing bias mitigation methods often achieve a high level of fairness at the cost of significant accuracy degradation. In response to thi... | 2405.08681 | title_snapshot |
Paper3065 | ACLNet: A Deep Learning Model for ACL Rupture Classification Combined with Bone Morphology | [
"Chao Liu",
"Xueqing Yu",
"Dingyu Wang",
"Tingting Jiang"
] | https://papers.miccai.org/miccai-2024/041-Paper3065.html | https://papers.miccai.org/miccai-2024/paper/3065_paper.pdf | 10.1007/978-3-031-72086-4_6 | https://rdcu.be/dV165 | https://papers.miccai.org/miccai-2024/supp/3065_supp.pdf | [
"Computer Aided Diagnosis, Treatment Response, and Outcome Prediction",
"Imaging-related Clinical Studies",
"Modalities - MRI"
] | [] | [] | 57-67 | @InProceedings{Liu_ACLNet_MICCAI2024,
author = { Liu, Chao and Yu, Xueqing and Wang, Dingyu and Jiang, Tingting},
title = { { ACLNet: A Deep Learning Model for ACL Rupture Classification Combined with Bone Morphology } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted ... | Magnetic Resonance Imaging (MRI) is widely used in diagnosing anterior cruciate ligament (ACL) injuries due to its ability to provide detailed image data. However, existing deep learning approaches often overlook additional factors beyond the image itself. In this study, we aim to bridge this gap by exploring the relat... | null | null |
Paper2216 | AcneAI: A new acne severity assessment method using digital images and deep learning | [
"Léa Gazeau",
"Hang Nguyen",
"Zung Nguyen",
"Mariia Lebedeva",
"Thanh Nguyen",
"Tat-Dat To",
"Jimmy Le Digabel",
"Jérome Filiol",
"Gwendal Josse",
"Clifford Perlis",
"Jonathan Wolfe"
] | https://papers.miccai.org/miccai-2024/042-Paper2216.html | https://papers.miccai.org/miccai-2024/paper/2216_paper.pdf | 10.1007/978-3-031-72086-4_7 | https://rdcu.be/dV166 | https://papers.miccai.org/miccai-2024/supp/2216_supp.pdf | [
"Computer Aided Diagnosis, Treatment Response, and Outcome Prediction",
"Clinical applications - Dermatology"
] | [] | [
"https://github.com/AIpourlapeau/acne04v2"
] | 68-78 | @InProceedings{Gaz_AcneAI_MICCAI2024,
author = { Gazeau, Léa and Nguyen, Hang and Nguyen, Zung and Lebedeva, Mariia and Nguyen, Thanh and To, Tat-Dat and Le Digabel, Jimmy and Filiol, Jérome and Josse, Gwendal and Perlis, Clifford and Wolfe, Jonathan},
title = { { AcneAI: A new acne severity assessment ... | In this paper we present a new AcneAI system that automatically analyses facial acne images in a precise way, detecting and scoring every single acne lesion within an image. Its workflow consists of three main steps: 1) segmentation of all acne and acne-like lesions, 2) scoring of each acne lesion, 3) combining individ... | null | null |
Paper2040 | Across-subject ensemble-learning alleviates the need for large samples for fMRI decoding | [
"Himanshu Aggarwal",
"Liza Al-Shikhley",
"Bertrand Thirion"
] | https://papers.miccai.org/miccai-2024/043-Paper2040.html | https://papers.miccai.org/miccai-2024/paper/2040_paper.pdf | 10.1007/978-3-031-72384-1_4 | https://rdcu.be/dV1Vd | https://papers.miccai.org/miccai-2024/supp/2040_supp.pdf | [
"Human-centred AI in Medical Imaging",
"Clinical applications - Neuroimaging - Others",
"Machine Learning - Data Efficient Learning",
"Machine Learning - Meta-learning",
"Machine Learning - Transfer Learning",
"Modalities - MRI"
] | [
"https://github.com/man-shu/ensemble-fmri"
] | [
"https://doi.org/10.5281/zenodo.12204275"
] | 35-45 | @InProceedings{Agg_Acrosssubject_MICCAI2024,
author = { Aggarwal, Himanshu and Al-Shikhley, Liza and Thirion, Bertrand},
title = { { Across-subject ensemble-learning alleviates the need for large samples for fMRI decoding } },
booktitle = {proceedings of Medical Image Computing and Computer Assi... | Decoding cognitive states from functional magnetic resonance imaging is central to understanding the functional organization of the brain. Within-subject decoding avoids between-subject correspondence problems but requires large sample sizes to make accurate predictions; obtaining such large sample sizes is both challe... | 2407.12056 | title_snapshot |
Paper3963 | Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label Noise | [
"Bidur Khanal",
"Tianhong Dai",
"Binod Bhattarai",
"Cristian Linte"
] | https://papers.miccai.org/miccai-2024/044-Paper3963.html | https://papers.miccai.org/miccai-2024/paper/3963_paper.pdf | 10.1007/978-3-031-72120-5_4 | https://rdcu.be/dV57Z | https://papers.miccai.org/miccai-2024/supp/3963_supp.pdf | [
"Machine Learning - Data Efficient Learning",
"Machine Learning - Active Learning",
"Machine Learning - Other"
] | [
"https://github.com/Bidur-Khanal/imbalanced-medical-active-label-cleaning.git"
] | [
"https://challenge.isic-archive.com/landing/2019/",
"https://zenodo.org/records/1214456"
] | 37-47 | @InProceedings{Kha_Active_MICCAI2024,
author = { Khanal, Bidur and Dai, Tianhong and Bhattarai, Binod and Linte, Cristian},
title = { { Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label Noise } },
booktitle = {proceedings o... | The robustness of supervised deep learning-based medical image classification is significantly undermined by label noise in the training data. Although several methods have been proposed to enhance classification performance in the presence of noisy labels, they face some challenges: 1) a struggle with class-imbalanced... | 2407.05973 | title_snapshot |
Paper3895 | AdaCBM: An Adaptive Concept Bottleneck Model for Explainable and Accurate Diagnosis | [
"Townim F. Chowdhury",
"Vu Minh Hieu Phan",
"Kewen Liao",
"Minh-Son To",
"Yutong Xie",
"Anton van den Hengel",
"Johan W. Verjans",
"Zhibin Liao"
] | https://papers.miccai.org/miccai-2024/045-Paper3895.html | https://papers.miccai.org/miccai-2024/paper/3895_paper.pdf | 10.1007/978-3-031-72117-5_4 | https://rdcu.be/dV53I | https://papers.miccai.org/miccai-2024/supp/3895_supp.pdf | [
"Machine Learning - Interpretability / Explainability",
"Machine Learning - Transfer Learning"
] | [
"https://github.com/AIML-MED/AdaCBM"
] | [] | 35-45 | @InProceedings{Cho_AdaCBM_MICCAI2024,
author = { Chowdhury, Townim F. and Phan, Vu Minh Hieu and Liao, Kewen and To, Minh-Son and Xie, Yutong and van den Hengel, Anton and Verjans, Johan W. and Liao, Zhibin},
title = { { AdaCBM: An Adaptive Concept Bottleneck Model for Explainable and Accurate Diagnosis... | The integration of vision-language models such as CLIP and Concept Bottleneck Models (CBMs) offers a promising approach to explaining deep neural network (DNN) decisions using concepts understandable by humans, addressing the black-box concern of DNNs. While CLIP provides both explainability and zero-shot classificatio... | 2408.02001 | title_snapshot |
Paper2585 | Adapting Pre-trained Generative Model to Medical Image for Data Augmentation | [
"Zhouhang Yuan",
"Zhengqing Fang",
"Zhengxing Huang",
"Fei Wu",
"Yu-Feng Yao",
"Yingming Li"
] | https://papers.miccai.org/miccai-2024/046-Paper2585.html | https://papers.miccai.org/miccai-2024/paper/2585_paper.pdf | 10.1007/978-3-031-72086-4_8 | https://rdcu.be/dV167 | null | [
"Computer Aided Diagnosis, Treatment Response, and Outcome Prediction",
"Clinical applications - Dermatology",
"Clinical applications - Ophthalmology",
"Machine Learning - Attention models",
"Machine Learning - Data Efficient Learning",
"Machine Learning - Foundation Models",
"Machine Learning - Transfe... | [
"https://github.com/YuanZhouhang/VQ-MAGE-Med"
] | [
"https://www.kaggle.com/datasets/kmader/skin-cancer-mnist-ham10000",
"https://odir2019.grand-challenge.org/dataset/"
] | 79-89 | @InProceedings{Yua_Adapting_MICCAI2024,
author = { Yuan, Zhouhang and Fang, Zhengqing and Huang, Zhengxing and Wu, Fei and Yao, Yu-Feng and Li, Yingming},
title = { { Adapting Pre-trained Generative Model to Medical Image for Data Augmentation } },
booktitle = {proceedings of Medical Image Compu... | Deep learning-based medical image recognition requires a large number of expert-annotated data. As medical image data is often scarce and class imbalanced, many researchers have tried to synthesize medical images as training samples. However, the quality of the generated data determines the effectiveness of the method,... | null | null |
Paper1423 | Adaptive Curriculum Query Strategy for Active Learning in Medical Image Classification | [
"Siteng Ma",
"Honghui Du",
"Kathleen M. Curran",
"Aonghus Lawlor",
"Ruihai Dong"
] | https://papers.miccai.org/miccai-2024/047-Paper1423.html | https://papers.miccai.org/miccai-2024/paper/1423_paper.pdf | 10.1007/978-3-031-72120-5_5 | https://rdcu.be/dV570 | null | [
"Machine Learning - Active Learning",
"Computer Aided Diagnosis, Treatment Response, and Outcome Prediction",
"Modalities - Histopathology",
"Modalities - MRI"
] | [
"https://github.com/HelenMa9998/Easy_hard_AL"
] | [
"https://www.adcis.net/en/third-party/messidor/",
"https://iciar2018-challenge.grand-challenge.org/Dataset/"
] | 48-57 | @InProceedings{Ma_Adaptive_MICCAI2024,
author = { Ma, Siteng and Du, Honghui and Curran, Kathleen M. and Lawlor, Aonghus and Dong, Ruihai},
title = { { Adaptive Curriculum Query Strategy for Active Learning in Medical Image Classification } },
booktitle = {proceedings of Medical Image Computing ... | Deep active learning (AL) is commonly used to reduce labeling costs in medical image analysis. Deep learning (DL) models typically exhibit a preference for learning from easy data and simple patterns before they learn from complex ones. However, existing AL methods often employ a fixed query strategy for sample selecti... | null | null |
Paper3846 | Adaptive Smooth Activation Function for Improved Organ Segmentation and Disease Diagnosis | [
"Koushik Biswas",
"Debesh Jha",
"Nikhil Kumar Tomar",
"Meghana Karri",
"Amit Reza",
"Gorkem Durak",
"Alpay Medetalibeyoglu",
"Matthew Antalek",
"Yury Velichko",
"Daniela Ladner",
"Amir Borhani",
"Ulas Bagci"
] | https://papers.miccai.org/miccai-2024/048-Paper3846.html | https://papers.miccai.org/miccai-2024/paper/3846_paper.pdf | 10.1007/978-3-031-72114-4_7 | https://rdcu.be/dV5Kb | null | [
"Image Segmentation",
"Clinical applications - Abdomen",
"Machine Learning - Other",
"Modalities - CT",
"Modalities - MRI"
] | [
"https://github.com/koushik313/ASAU"
] | [] | 65-74 | @InProceedings{Bis_Adaptive_MICCAI2024,
author = { Biswas, Koushik and Jha, Debesh and Tomar, Nikhil Kumar and Karri, Meghana and Reza, Amit and Durak, Gorkem and Medetalibeyoglu, Alpay and Antalek, Matthew and Velichko, Yury and Ladner, Daniela and Borhani, Amir and Bagci, Ulas},
title = { { Adaptive S... | The design of activation functions constitutes a cornerstone for deep learning (DL) applications, exerting a profound influence on the performance and capabilities of neural networks. This influence stems from their ability to introduce non-linearity into the network architecture. By doing so, activation functions empo... | 2312.11480 | title_judge |
Paper3688 | Adaptive Subtype and Stage Inference for Alzheimer’s Disease | [
"Xinkai Wang",
"Yonggang Shi"
] | https://papers.miccai.org/miccai-2024/049-Paper3688.html | https://papers.miccai.org/miccai-2024/paper/3688_paper.pdf | 10.1007/978-3-031-72384-1_5 | https://rdcu.be/dV1Ve | null | [
"Image-based Personalised Medicine",
"Clinical applications - Neuroimaging - Others",
"Computational (Integrative) Pathology",
"Machine Learning - Meta-learning",
"Machine Learning - Model Generalizability / Federated Learning",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised Representation Learning... | [
"https://github.com/x5wang/Adaptive-Subtype-and-Stage-Inference"
] | [] | 46-55 | @InProceedings{Wan_Adaptive_MICCAI2024,
author = { Wang, Xinkai and Shi, Yonggang},
title = { { Adaptive Subtype and Stage Inference for Alzheimer’s Disease } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
year = {2024},
... | Subtype and Stage Inference (SuStaIn) is a useful Event-based Model for capturing both the temporal and the phenotypical patterns for any progressive disorders, which is essential for understanding the heterogeneous nature of such diseases. However, this model cannot capture subtypes with different progression rates wi... | null | null |
Paper0173 | Advancing Brain Imaging Analysis Step-by-step via Progressive Self-paced Learning | [
"Yanwu Yang",
"Hairui Chen",
"Jiesi Hu",
"Xutao Guo",
"Ting Ma"
] | https://papers.miccai.org/miccai-2024/050-Paper0173.html | https://papers.miccai.org/miccai-2024/paper/0173_paper.pdf | 10.1007/978-3-031-72120-5_6 | https://rdcu.be/dV571 | https://papers.miccai.org/miccai-2024/supp/0173_supp.pdf | [
"Machine Learning - Data Efficient Learning",
"Computer Aided Diagnosis, Treatment Response, and Outcome Prediction",
"Modalities - MRI"
] | [
"https://github.com/Hrychen7/PSPD"
] | [] | 58-68 | @InProceedings{Yan_Advancing_MICCAI2024,
author = { Yang, Yanwu and Chen, Hairui and Hu, Jiesi and Guo, Xutao and Ma, Ting},
title = { { Advancing Brain Imaging Analysis Step-by-step via Progressive Self-paced Learning } },
booktitle = {proceedings of Medical Image Computing and Computer Assiste... | Recent advancements in deep learning have shifted the development of brain imaging analysis. However, several challenges remain, such as heterogeneity, individual variations, and the contradiction between the high dimensionality and small size of brain imaging datasets. These issues complicate the learning process, pre... | 2407.16128 | title_snapshot |
Paper3227 | Advancing H&E-to-IHC Virtual Staining with Task-Specific Domain Knowledge for HER2 Scoring | [
"Qiong Peng",
"Weiping Lin",
"Yihuang Hu",
"Ailisi Bao",
"Chenyu Lian",
"Weiwei Wei",
"Meng Yue",
"Jingxin Liu",
"Lequan Yu",
"Liansheng Wang"
] | https://papers.miccai.org/miccai-2024/051-Paper3227.html | https://papers.miccai.org/miccai-2024/paper/3227_paper.pdf | 10.1007/978-3-031-72083-3_1 | https://rdcu.be/dY6h9 | null | [
"Computational (Integrative) Pathology",
"Clinical applications - Breast",
"Image Formation and Reconstruction",
"Modalities - Histopathology"
] | [
"https://github.com/balball/TDKstain"
] | [
"https://github.com/balball/TDKstain"
] | 3-13 | @InProceedings{Pen_Advancing_MICCAI2024,
author = { Peng, Qiong and Lin, Weiping and Hu, Yihuang and Bao, Ailisi and Lian, Chenyu and Wei, Weiwei and Yue, Meng and Liu, Jingxin and Yu, Lequan and Wang, Liansheng},
title = { { Advancing H&E-to-IHC Virtual Staining with Task-Specific Domain Knowledge for ... | The assessment of HER2 expression is crucial in diagnosing breast cancer. Staining pathological tissues with immunohistochemistry (IHC) is a critically pivotal step in the assessment procedure, while it is expensive and time-consuming. Recently, generative models have emerged as a novel paradigm for virtual staining fr... | null | null |
Paper1045 | Advancing Sensorless Freehand 3D Ultrasound Reconstruction with a Novel Coupling Pad | [
"Ling Dai",
"Kaitao Zhao",
"Zhongyu Li",
"Jihua Zhu",
"Libin Liang"
] | https://papers.miccai.org/miccai-2024/052-Paper1045.html | https://papers.miccai.org/miccai-2024/paper/1045_paper.pdf | 10.1007/978-3-031-72083-3_52 | https://rdcu.be/dY6jx | https://papers.miccai.org/miccai-2024/supp/1045_supp.pdf | [
"Modalities - Ultrasound",
"Image Formation and Reconstruction"
] | [] | [] | 559-569 | @InProceedings{Dai_Advancing_MICCAI2024,
author = { Dai, Ling and Zhao, Kaitao and Li, Zhongyu and Zhu, Jihua and Liang, Libin},
title = { { Advancing Sensorless Freehand 3D Ultrasound Reconstruction with a Novel Coupling Pad } },
booktitle = {proceedings of Medical Image Computing and Computer ... | Sensorless freehand 3D ultrasound (US) reconstruction poses a significant challenge, yet it holds considerable importance in improving the accessibility of 3D US applications in clinics. Current mainstream solutions, relying on inertial measurement units or deep learning, encounter issues like cumulative drift. To over... | null | null |
Paper0165 | Advancing Text-Driven Chest X-Ray Generation with Policy-Based Reinforcement Learning | [
"Woojung Han",
"Chanyoung Kim",
"Dayun Ju",
"Yumin Shim",
"Seong Jae Hwang"
] | https://papers.miccai.org/miccai-2024/053-Paper0165.html | https://papers.miccai.org/miccai-2024/paper/0165_paper.pdf | 10.1007/978-3-031-72384-1_6 | https://rdcu.be/dV1Vf | https://papers.miccai.org/miccai-2024/supp/0165_supp.zip | [
"Modalities - Integration of Imaging and Non-imaging Data",
"Image Formation and Reconstruction"
] | [
"https://github.com/MICV-yonsei/CXRL"
] | [] | 56-66 | @InProceedings{Han_Advancing_MICCAI2024,
author = { Han, Woojung and Kim, Chanyoung and Ju, Dayun and Shim, Yumin and Hwang, Seong Jae},
title = { { Advancing Text-Driven Chest X-Ray Generation with Policy-Based Reinforcement Learning } },
booktitle = {proceedings of Medical Image Computing and ... | Recent advances in text-conditioned image generation diffusion models have begun paving the way for new opportunities in modern medical domain, in particular, generating Chest X-rays (CXRs) from diagnostic reports. Nonetheless, to further drive the diffusion models to generate CXRs that faithfully reflect the complexit... | 2403.06516 | title_snapshot |
Paper2030 | Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center Dataset | [
"Hongqiu Wang",
"Xiangde Luo",
"Wu Chen",
"Qingqing Tang",
"Mei Xin",
"Qiong Wang",
"Lei Zhu"
] | https://papers.miccai.org/miccai-2024/054-Paper2030.html | https://papers.miccai.org/miccai-2024/paper/2030_paper.pdf | 10.1007/978-3-031-72114-4_8 | https://rdcu.be/dV5Kc | null | [
"Image Segmentation",
"Clinical applications - Ophthalmology",
"Machine Learning - Active Learning",
"Machine Learning - Data Efficient Learning"
] | [
"https://github.com/whq-xxh/SFADA-UWF-SLO"
] | [] | 75-85 | @InProceedings{Wan_Advancing_MICCAI2024,
author = { Wang, Hongqiu and Luo, Xiangde and Chen, Wu and Tang, Qingqing and Xin, Mei and Wang, Qiong and Zhu, Lei},
title = { { Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center Dataset } },
booktit... | Accurate vessel segmentation in Ultra-Wide-Field Scanning Laser Ophthalmoscopy (UWF-SLO) images is crucial for diagnosing retinal diseases. Although recent techniques have shown encouraging outcomes in vessel segmentation, models trained on one medical dataset often underperform on others due to domain shifts. Meanwhil... | 2406.13645 | title_snapshot |
Paper1749 | Adversarial Diffusion Model for Domain-Adaptive Depth Estimation in Bronchoscopic Navigation | [
"Yiguang Yang",
"Guochen Ning",
"Changhao Zhong",
"Hongen Liao"
] | https://papers.miccai.org/miccai-2024/055-Paper1749.html | https://papers.miccai.org/miccai-2024/paper/1749_paper.pdf | 10.1007/978-3-031-72089-5_5 | https://rdcu.be/dV5vV | https://papers.miccai.org/miccai-2024/supp/1749_supp.pdf | [
"Modalities - Endoscope",
"Clinical applications - Lung",
"Image-Guided Interventions and Surgery",
"Machine Learning - Transfer Learning"
] | [] | [] | 46-56 | @InProceedings{Yan_Adversarial_MICCAI2024,
author = { Yang, Yiguang and Ning, Guochen and Zhong, Changhao and Liao, Hongen},
title = { { Adversarial Diffusion Model for Domain-Adaptive Depth Estimation in Bronchoscopic Navigation } },
booktitle = {proceedings of Medical Image Computing and Compu... | In bronchoscopic navigation, depth estimation has emerged as a promising method with higher robustness for localizing camera and obtaining scene geometry. While many supervised approaches have shown success for natural images, the scarcity of depth annotations limits their deployment in bronchoscopic scenarios. To addr... | null | null |
Paper0803 | Affinity Learning Based Brain Function Representation for Disease Diagnosis | [
"Mengjun Liu",
"Zhiyun Song",
"Dongdong Chen",
"Xin Wang",
"Zixu Zhuang",
"Manman Fei",
"Lichi Zhang",
"Qian Wang"
] | https://papers.miccai.org/miccai-2024/056-Paper0803.html | https://papers.miccai.org/miccai-2024/paper/0803_paper.pdf | 10.1007/978-3-031-72069-7_2 | https://rdcu.be/dV1Ml | https://papers.miccai.org/miccai-2024/supp/0803_supp.pdf | [
"Clinical applications - Neuroimaging - Functional Brain Networks",
"Computer Aided Diagnosis, Treatment Response, and Outcome Prediction",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised Representation Learning",
"Modalities - MRI"
] | [] | [] | 14-23 | @InProceedings{Liu_Affinity_MICCAI2024,
author = { Liu, Mengjun and Song, Zhiyun and Chen, Dongdong and Wang, Xin and Zhuang, Zixu and Fei, Manman and Zhang, Lichi and Wang, Qian},
title = { { Affinity Learning Based Brain Function Representation for Disease Diagnosis } },
booktitle = {proceedin... | Resting-state functional magnetic resonance imaging (rs-fMRI) serves as a potent means to quantify brain functional connectivity (FC), which holds potential in diagnosing diseases. However, conventional FC measures may fall short in encapsulating the intricate functional dynamics of the brain; for instance, FC computed... | null | null |
Paper3005 | Airway segmentation based on topological structure enhancement using multi-task learning | [
"Xuan Yang",
"Lingyu Chen",
"Yuchao Zheng",
"Longfei Ma",
"Fang Chen",
"Guochen Ning",
"Hongen Liao"
] | https://papers.miccai.org/miccai-2024/057-Paper3005.html | https://papers.miccai.org/miccai-2024/paper/3005_paper.pdf | 10.1007/978-3-031-72114-4_9 | https://rdcu.be/dV5Kd | https://papers.miccai.org/miccai-2024/supp/3005_supp.pdf | [
"Image Segmentation",
"Clinical applications - Lung",
"Modalities - CT"
] | [
"https://github.com/xyang-11/airway_seg"
] | [] | 86-95 | @InProceedings{Yan_Airway_MICCAI2024,
author = { Yang, Xuan and Chen, Lingyu and Zheng, Yuchao and Ma, Longfei and Chen, Fang and Ning, Guochen and Liao, Hongen},
title = { { Airway segmentation based on topological structure enhancement using multi-task learning } },
booktitle = {proceedings of... | Airway segmentation in chest computed tomography (CT) images is critical for tracheal disease diagnosis and surgical navigation. However, airway segmentation is challenging due to complex tree structures and branches of different sizes. To enhance airway integrity and reduce fractures during bronchus segmentation, we p... | null | null |
Paper2017 | Algebraic Sphere Surface Fitting for Accurate and Efficient Mesh Reconstruction from Cine CMR Images | [
"Jin He",
"Weizhou Liu",
"Shifeng Zhao",
"Yun Tian",
"Shuo Wang"
] | https://papers.miccai.org/miccai-2024/058-Paper2017.html | https://papers.miccai.org/miccai-2024/paper/2017_paper.pdf | 10.1007/978-3-031-72378-0_16 | https://rdcu.be/dVZei | https://papers.miccai.org/miccai-2024/supp/2017_supp.zip | [
"Clinical applications - Cardiac",
"Computational Anatomy and Physiology",
"Modalities - MRI",
"Visualization in Biomedical Imaging"
] | [
"https://github.com/hejin9/algebraic-sphere-surface-fitting"
] | [] | 169-178 | @InProceedings{He_Algebraic_MICCAI2024,
author = { He, Jin and Liu, Weizhou and Zhao, Shifeng and Tian, Yun and Wang, Shuo},
title = { { Algebraic Sphere Surface Fitting for Accurate and Efficient Mesh Reconstruction from Cine CMR Images } },
booktitle = {proceedings of Medical Image Computing a... | Accurate 3D modeling of the ventricles through cine cardiovascular magnetic resonance (CMR) imaging benefits precise clinical assessment of cardiac morphology and motion. However, the existing short-axis stacks exhibit low spatial resolution in the inter-slice orientation compared to the intra-slice direction, resultin... | null | null |
Paper2178 | Algorithmic Fairness in Lesion Classification by Mitigating Class Imbalance and Skin Tone Bias | [
"Faizanuddin Ansari",
"Tapabrata Chakraborti",
"Swagatam Das"
] | https://papers.miccai.org/miccai-2024/059-Paper2178.html | https://papers.miccai.org/miccai-2024/paper/2178_paper.pdf | 10.1007/978-3-031-72378-0_35 | https://rdcu.be/dVZeC | null | [
"Clinical applications - Dermatology",
"Machine Learning - Algorithmic Fairness",
"Machine Learning - Transfer Learning"
] | [
"https://github.com/fa-submit/Submission_M"
] | [] | 373-382 | @InProceedings{Ans_Algorithmic_MICCAI2024,
author = { Ansari, Faizanuddin and Chakraborti, Tapabrata and Das, Swagatam},
title = { { Algorithmic Fairness in Lesion Classification by Mitigating Class Imbalance and Skin Tone Bias } },
booktitle = {proceedings of Medical Image Computing and Compute... | Deep learning models have shown considerable promise in the classification of skin lesions. However, a notable challenge arises from their inherent bias towards dominant skin tones and the issue of imbalanced class representation. This study introduces a novel data augmentation technique designed to address these limit... | null | null |
Paper1172 | Aligning and Restoring Imperfect ssEM images for Continuity Reconstruction | [
"Yanan Lv",
"Haoze Jia",
"Xi Chen",
"Haiyang Yan",
"Hua Han"
] | https://papers.miccai.org/miccai-2024/060-Paper1172.html | https://papers.miccai.org/miccai-2024/paper/1172_paper.pdf | 10.1007/978-3-031-72069-7_51 | https://rdcu.be/dV1PX | null | [
"Image Registration",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised Representation Learning",
"Modalities - Microscopy"
] | [
"https://github.com/lvyanan525/Aligning-and-Restoring-Imperfect-ssEM-images"
] | [] | 543-552 | @InProceedings{Lv_Aligning_MICCAI2024,
author = { Lv, Yanan and Jia, Haoze and Chen, Xi and Yan, Haiyang and Han, Hua},
title = { { Aligning and Restoring Imperfect ssEM images for Continuity Reconstruction } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted Interventi... | The serial section electron microscopy reconstruction method is commonly used in large volume reconstruction of biological tissue, but the inevitable section damage brings challenges to volume reconstruction. The section damage may result in imperfect section alignment and affect the subsequent neuron segmentation and ... | null | null |
Paper0117 | Aligning Human Knowledge with Visual Concepts Towards Explainable Medical Image Classification | [
"Yunhe Gao",
"Difei Gu",
"Mu Zhou",
"Dimitris Metaxas"
] | https://papers.miccai.org/miccai-2024/061-Paper0117.html | https://papers.miccai.org/miccai-2024/paper/0117_paper.pdf | 10.1007/978-3-031-72117-5_5 | https://rdcu.be/dV53J | null | [
"Machine Learning - Interpretability / Explainability",
"Computer Aided Diagnosis, Treatment Response, and Outcome Prediction"
] | [
"https://github.com/yhygao/Explicd"
] | [] | 46-56 | @InProceedings{Gao_Aligning_MICCAI2024,
author = { Gao, Yunhe and Gu, Difei and Zhou, Mu and Metaxas, Dimitris},
title = { { Aligning Human Knowledge with Visual Concepts Towards Explainable Medical Image Classification } },
booktitle = {proceedings of Medical Image Computing and Computer Assist... | Although explainability is essential in the clinical diagnosis, most deep learning models still function as black boxes without elucidating their decision-making process. In this study, we investigate the explainable model development that can mimic the decision-making process of human experts by fusing the domain know... | 2406.05596 | title_snapshot |
Paper1358 | Aligning Medical Images with General Knowledge from Large Language Models | [
"Xiao Fang",
"Yi Lin",
"Dong Zhang",
"Kwang-Ting Cheng",
"Hao Chen"
] | https://papers.miccai.org/miccai-2024/062-Paper1358.html | https://papers.miccai.org/miccai-2024/paper/1358_paper.pdf | 10.1007/978-3-031-72117-5_6 | https://rdcu.be/dV53K | null | [
"Computer Aided Diagnosis, Treatment Response, and Outcome Prediction",
"Machine Learning - Interpretability / Explainability"
] | [
"https://github.com/xiaofang007/ViP"
] | [
"https://derm.cs.sfu.ca/Welcome.html",
"https://data.mendeley.com/datasets/rscbjbr9sj/2"
] | 57-67 | @InProceedings{Fan_Aligning_MICCAI2024,
author = { Fang, Xiao and Lin, Yi and Zhang, Dong and Cheng, Kwang-Ting and Chen, Hao},
title = { { Aligning Medical Images with General Knowledge from Large Language Models } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted Int... | Pre-trained large vision-language models (VLMs) like CLIP have revolutionized visual representation learning using natural language as supervisions, and have demonstrated promising generalization ability. In this work, we propose ViP, a novel visual symptom-guided prompt learning framework for medical image analysis, w... | 2409.00341 | title_snapshot |
Paper0787 | All-In-One Medical Image Restoration via Task-Adaptive Routing | [
"Zhiwen Yang",
"Haowei Chen",
"Ziniu Qian",
"Yang Yi",
"Hui Zhang",
"Dan Zhao",
"Bingzheng Wei",
"Yan Xu"
] | https://papers.miccai.org/miccai-2024/063-Paper0787.html | https://papers.miccai.org/miccai-2024/paper/0787_paper.pdf | 10.1007/978-3-031-72104-5_7 | https://rdcu.be/dV5BB | https://papers.miccai.org/miccai-2024/supp/0787_supp.pdf | [
"Image Formation and Reconstruction",
"Modalities - CT",
"Modalities - MRI",
"Modalities - PET/SPECT"
] | [
"https://github.com/Yaziwel/All-In-One-Medical-Image-Restoration-via-Task-Adaptive-Routing.git"
] | [] | 67-77 | @InProceedings{Yan_AllInOne_MICCAI2024,
author = { Yang, Zhiwen and Chen, Haowei and Qian, Ziniu and Yi, Yang and Zhang, Hui and Zhao, Dan and Wei, Bingzheng and Xu, Yan},
title = { { All-In-One Medical Image Restoration via Task-Adaptive Routing } },
booktitle = {proceedings of Medical Image Co... | Although single-task medical image restoration (MedIR) has witnessed remarkable success, the limited generalizability of these methods poses a substantial obstacle to wider application. In this paper, we focus on the task of all-in-one medical image restoration, aiming to address multiple distinct MedIR tasks with a si... | 2405.19769 | title_snapshot |
Paper2801 | AMONuSeg: A Histological Dataset for African Multi-Organ Nuclei Semantic Segmentation | [
"Hasnae Zerouaoui",
"Gbenga Peter Oderinde",
"Rida Lefdali",
"Karima Echihabi",
"Stephen Peter Akpulu",
"Nosereme Abel Agbon",
"Abraham Sunday Musa",
"Yousef Yeganeh",
"Azade Farshad",
"Nassir Navab"
] | https://papers.miccai.org/miccai-2024/064-Paper2801.html | https://papers.miccai.org/miccai-2024/paper/2801_paper.pdf | 10.1007/978-3-031-72114-4_10 | https://rdcu.be/dV5Ke | null | [
"Image Segmentation",
"Modalities - Microscopy"
] | [
"https://github.com/zerouaoui/AMONUSEG"
] | [
"https://github.com/zerouaoui/AMONUSEG"
] | 96-106 | @InProceedings{Zer_AMONuSeg_MICCAI2024,
author = { Zerouaoui, Hasnae and Oderinde, Gbenga Peter and Lefdali, Rida and Echihabi, Karima and Akpulu, Stephen Peter and Agbon, Nosereme Abel and Musa, Abraham Sunday and Yeganeh, Yousef and Farshad, Azade and Navab, Nassir},
title = { { AMONuSeg: A Histologic... | Nuclei semantic segmentation is a key component for advancing machine learning and deep learning applications in digital pathology. However, most existing segmentation models are trained and tested on high-quality data acquired with expensive equipment, such as whole slide scanners, which are not accessible to most pat... | null | null |
Paper3820 | An approach to building foundation models for brain image analysis | [
"Davood Karimi"
] | https://papers.miccai.org/miccai-2024/065-Paper3820.html | https://papers.miccai.org/miccai-2024/paper/3820_paper.pdf | 10.1007/978-3-031-72390-2_40 | https://rdcu.be/dY6f9 | https://papers.miccai.org/miccai-2024/supp/3820_supp.pdf | [
"Machine Learning - Foundation Models",
"Clinical applications - Neuroimaging - Others",
"Machine Learning - Other",
"Machine Learning - Transfer Learning"
] | [] | [] | 421-431 | @InProceedings{Kar_An_MICCAI2024,
author = { Karimi, Davood},
title = { { An approach to building foundation models for brain image analysis } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
year = {2024},
publisher = {Sp... | Existing machine learning methods for brain image analysis are mostly based on supervised training. They require large labeled datasets, which can be costly or impossible to obtain. Moreover, the trained models are useful only for the narrow task defined by the labels. In this work, we developed a new method, based on ... | null | null |
Paper1289 | An Empirical Study on the Fairness of Foundation Models for Multi-Organ Image Segmentation | [
"Qing Li",
"Yizhe Zhang",
"Yan Li",
"Jun Lyu",
"Meng Liu",
"Longyu Sun",
"Mengting Sun",
"Qirong Li",
"Wenyue Mao",
"Xinran Wu",
"Yajing Zhang",
"Yinghua Chu",
"Shuo Wang",
"Chengyan Wang"
] | https://papers.miccai.org/miccai-2024/066-Paper1289.html | https://papers.miccai.org/miccai-2024/paper/1289_paper.pdf | 10.1007/978-3-031-72390-2_41 | https://rdcu.be/dY6ga | https://papers.miccai.org/miccai-2024/supp/1289_supp.pdf | [
"Machine Learning - Foundation Models",
"Image Segmentation",
"Machine Learning - Algorithmic Fairness"
] | [] | [] | 432-442 | @InProceedings{Li_An_MICCAI2024,
author = { Li, Qing and Zhang, Yizhe and Li, Yan and Lyu, Jun and Liu, Meng and Sun, Longyu and Sun, Mengting and Li, Qirong and Mao, Wenyue and Wu, Xinran and Zhang, Yajing and Chu, Yinghua and Wang, Shuo and Wang, Chengyan},
title = { { An Empirical Study on the Fairne... | The segmentation foundation model, e.g., Segment Anything Model (SAM), has attracted increasing interest in the medical image community. Early pioneering studies primarily concentrated on assessing and improving SAM’s performance from the perspectives of overall accuracy and efficiency, yet little attention was given t... | 2406.12646 | title_snapshot |
Paper1606 | An Evaluation of State-of-the-Art Projectors in the Presence of Noise and Nonlinearity in the Beer-Lambert Law | [
"Shiyu Xie",
"Kai Zhang",
"Alireza Entezari"
] | https://papers.miccai.org/miccai-2024/067-Paper1606.html | https://papers.miccai.org/miccai-2024/paper/1606_paper.pdf | 10.1007/978-3-031-72104-5_8 | https://rdcu.be/dV5BC | null | [
"Modalities - CT"
] | [
"https://github.com/ShiyuXie0116/Evaluation-of-Projectors-Noise-Nonlinearity"
] | [
"https://github.com/ashkarin/forbild-gen"
] | 78-87 | @InProceedings{Xie_An_MICCAI2024,
author = { Xie, Shiyu and Zhang, Kai and Entezari, Alireza},
title = { { An Evaluation of State-of-the-Art Projectors in the Presence of Noise and Nonlinearity in the Beer-Lambert Law } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted... | Efficient computation of forward and back projection is key to scalability of iterative methods for low dose CT imaging at resolutions needed in clinical applications. State-of-the-art projectors provide computationally-efficient approximations to X-ray optics calculations in the forward model that strike a balance bet... | null | null |
Paper3035 | An MR-Compatible Virtual Reality System for Assessing Neuronal Plasticity of Sensorimotor Neurons and Mirror Neurons | [
"Xiaocheng Wang",
"D. B. Mekbib",
"Tian Zhou",
"Junming Zhu",
"Li Zhang",
"Ruidong Cheng",
"Jianmin Zhang",
"Xiangming Ye",
"Dongrong Xu"
] | https://papers.miccai.org/miccai-2024/068-Paper3035.html | https://papers.miccai.org/miccai-2024/paper/3035_paper.pdf | 10.1007/978-3-031-72089-5_6 | https://rdcu.be/dV5vW | null | [
"Surgical Visualization and Mixed/Augmented/Virtual Reality",
"Clinical applications - Neuroimaging - Brain Development",
"Clinical applications - Neuroimaging - Others",
"Imaging-related Clinical Studies",
"Modalities - MRI"
] | [] | [] | 57-66 | @InProceedings{Wan_An_MICCAI2024,
author = { Wang, Xiaocheng and Mekbib, D. B. and Zhou, Tian and Zhu, Junming and Zhang, Li and Cheng, Ruidong and Zhang, Jianmin and Ye, Xiangming and Xu, Dongrong},
title = { { An MR-Compatible Virtual Reality System for Assessing Neuronal Plasticity of Sensorimotor Ne... | Virtual reality (VR) assisted rehabilitation system is being used more commonly in supplementing upper extremities (UE) functional rehabilitation. Mirror therapy (MT) is reportedly a useful training in encouraging motor functional recovery. However, the majority of current systems are not compatible with magnetic reson... | null | null |
Paper0233 | An Organism Starts with a Single Pix-Cell: A Neural Cellular Diffusion for High-Resolution Image Synthesis | [
"Marawan Elbatel",
"Konstantinos Kamnitsas",
"Xiaomeng Li"
] | https://papers.miccai.org/miccai-2024/069-Paper0233.html | https://papers.miccai.org/miccai-2024/paper/0233_paper.pdf | 10.1007/978-3-031-72378-0_61 | https://rdcu.be/dVZi0 | https://papers.miccai.org/miccai-2024/supp/0233_supp.pdf | [
"Clinical applications - Ophthalmology",
"Computer Aided Diagnosis, Treatment Response, and Outcome Prediction"
] | [
"https://github.com/xmed-lab/GeCA"
] | [] | 656-666 | @InProceedings{Elb_An_MICCAI2024,
author = { Elbatel, Marawan and Kamnitsas, Konstantinos and Li, Xiaomeng},
title = { { An Organism Starts with a Single Pix-Cell: A Neural Cellular Diffusion for High-Resolution Image Synthesis } },
booktitle = {proceedings of Medical Image Computing and Compute... | Generative modeling seeks to approximate the statistical properties of real data, enabling synthesis of new data that closely resembles the original distribution. Generative Adversarial Networks (GANs) and Denoising Diffusion Probabilistic Models (DDPMs) represent significant advancements in generative modeling, drawin... | 2407.03018 | title_snapshot |
Paper1781 | An Uncertainty-guided Tiered Self-training Framework for Active Source-free Domain Adaptation in Prostate Segmentation | [
"Zihao Luo",
"Xiangde Luo",
"Zijun Gao",
"Guotai Wang"
] | https://papers.miccai.org/miccai-2024/070-Paper1781.html | https://papers.miccai.org/miccai-2024/paper/1781_paper.pdf | 10.1007/978-3-031-72114-4_11 | https://rdcu.be/dV5Kg | null | [
"Image Segmentation",
"Machine Learning - Active Learning",
"Machine Learning - Data Efficient Learning",
"Machine Learning - Model Generalizability / Federated Learning",
"Machine Learning - Semi-/Weakly-/Un-/Self-supervised Representation Learning",
"Machine Learning - Transfer Learning"
] | [] | [] | 107-117 | @InProceedings{Luo_An_MICCAI2024,
author = { Luo, Zihao and Luo, Xiangde and Gao, Zijun and Wang, Guotai},
title = { { An Uncertainty-guided Tiered Self-training Framework for Active Source-free Domain Adaptation in Prostate Segmentation } },
booktitle = {proceedings of Medical Image Computing a... | Deep learning models have exhibited remarkable efficacy in accurately delineating the prostate for diagnosis and treatment of prostate diseases, but challenges persist in achieving robust generalization across different medical centers. Source-free Domain Adaptation (SFDA) is a promising technique to adapt deep segment... | 2407.02893 | title_snapshot |
End of preview. Expand in Data Studio
README.md exists but content is empty.
- Downloads last month
- -