Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention最新文献

{"title":"Evaluating the Quality of Brain MRI Generators.","authors":"Jiaqi Wu, Wei Peng, Binxu Li, Yu Zhang, Kilian M Pohl","doi":"10.1007/978-3-031-72117-5_28","DOIUrl":"10.1007/978-3-031-72117-5_28","url":null,"abstract":"<p><p>Deep learning models generating structural brain MRIs have the potential to significantly accelerate discovery of neuroscience studies. However, their use has been limited in part by the way their quality is evaluated. Most evaluations of generative models focus on metrics originally designed for natural images (such as structural similarity index and Fréchet inception distance). As we show in a comparison of 6 state-of-the-art generative models trained and tested on over 3000 MRIs, these metrics are sensitive to the experimental setup and inadequately assess how well brain MRIs capture macrostructural properties of brain regions (a.k.a., anatomical plausibility). This shortcoming of the metrics results in inconclusive findings even when qualitative differences between the outputs of models are evident. We therefore propose a framework for evaluating models generating brain MRIs, which requires uniform processing of the real MRIs, standardizing the implementation of the models, and automatically segmenting the MRIs generated by the models. The segmentations are used for quantifying the plausibility of anatomy displayed in the MRIs. To ensure meaningful quantification, it is crucial that the segmentations are highly reliable. Our framework rigorously checks this reliability, a step often overlooked by prior work. Only 3 of the 6 generative models produced MRIs, of which at least 95% had highly reliable segmentations. More importantly, the assessment of each model by our framework is in line with qualitative assessments, reinforcing the validity of our approach. The code of this framework is available via https://github.com/jiaqiw01/MRIAnatEval.git.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15010 ","pages":"297-307"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12066240/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144056478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Super-Field MRI Synthesis for Infant Brains Enhanced by Dual Channel Latent Diffusion.","authors":"Austin Tapp, Can Zhao, Holger R Roth, Jeffrey Tanedo, Syed Muhammad Anwar, Niall J Bourke, Joseph Hajnal, Victoria Nankabirwa, Sean Deoni, Natasha Lepore, Marius George Linguraru","doi":"10.1007/978-3-031-72384-1_42","DOIUrl":"10.1007/978-3-031-72384-1_42","url":null,"abstract":"<p><p>In resource-limited settings, portable ultra-low-field (uLF, i.e., 0.064T) magnetic resonance imaging (MRI) systems expand accessibility of radiological scanning, particularly for low-income areas as well as underserved populations like neonates and infants. However, compared to high-field (HF, e.g., ≥ 1.5T) systems, inferior image quality in uLF scanning poses challenges for research and clinical use. To address this, we introduce Super-Field Network (SFNet), a custom swinUNETRv2 with generative adversarial network components that uses uLF MRIs to generate super-field (SF) images comparable to HF MRIs. We acquired a cohort of infant data (n=30, aged 0-2 years) with paired uLF-HF MRI data from a resource-limited setting with an underrepresented population in research. To enhance the small dataset, we present a novel use of latent diffusion to create dual-channel (uLF-HF) paired MRIs. We compare SFNet with state-of-the-art synthesis methods by HF-SF image similarity perceptual scores and by automated HF and SF segmentations of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The best performance was achieved by SFNet trained on the latent diffusion enhanced dataset yielding state-of-the-art results in Fréchet inception distance at 9.08 ± 1.21, perceptual similarity at 0.11 ± 0.01, and PSNR at 22.64 ± 1.31. True HF and SF segmentations had a strong overlap with Dice similarity coefficients of 0.71 ± 0.1, 0.79 ± 0.2, and 0.73 ± 0.08 for WM, GM, and CSF, respectively, in the developing infant brain with incomplete myelination, and displayed 166%, 107%, and 106% improvement over respective uLF-based segmentation metrics. SF MRI supports health equity by enhancing the clinical use of uLF imaging systems and improving the diagnostic capabilities of low-cost portable MRI systems in resource-limited settings and for underserved populations. Our code is made openly available at https://github.com/AustinTapp/SFnet.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15003 ","pages":"444-454"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12033166/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144053815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Volume-optimal persistence homological scaffolds of hemodynamic networks covary with MEG theta-alpha aperiodic dynamics.","authors":"Nghi Nguyen, Tao Hou, Enrico Amico, Jingyi Zheng, Huajun Huang, Alan D Kaplan, Giovanni Petri, Joaquín Goñi, Yize Zhao, Duy Duong-Tran, Li Shen","doi":"10.1007/978-3-031-72384-1_49","DOIUrl":"10.1007/978-3-031-72384-1_49","url":null,"abstract":"<p><p>Higher-order properties of functional magnetic resonance imaging (fMRI) induced connectivity have been shown to unravel many exclusive topological and dynamical insights beyond pairwise interactions. Nonetheless, whether these fMRI-induced higher-order properties play a role in disentangling other neuroimaging modalities' insights remains largely unexplored and poorly understood. In this work, by analyzing fMRI data from the Human Connectome Project Young Adult dataset using persistent homology, we discovered that the volume-optimal persistence homological scaffolds of fMRI-based functional connectomes exhibited conservative topological reconfigurations from the resting state to attentional task-positive state. Specifically, while reflecting the extent to which each cortical region contributed to functional cycles following different cognitive demands, these reconfigurations were constrained such that the spatial distribution of cavities in the connectome is relatively conserved. Most importantly, such level of contributions covaried with powers of aperiodic activities mostly within the theta-alpha (4-12 Hz) band measured by magnetoencephalography (MEG). This comprehensive result suggests that fMRI-induced hemodynamics and MEG theta-alpha aperiodic activities are governed by the same functional constraints specific to each cortical morpho-structure. Methodologically, our work paves the way toward an innovative computing paradigm in multimodal neuroimaging topological learning. The code for our analyses is provided in https://github.com/ngcaonghi/scaffold_noise.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15003 ","pages":"519-529"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11816146/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143416610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Consecutive-Contrastive Spherical U-Net: Enhancing Reliability of Individualized Functional Brain Parcellation for Short-Duration fMRI Scans.","authors":"Dan Hu, Kangfu Han, Jiale Cheng, Gang Li","doi":"10.1007/978-3-031-72069-7_9","DOIUrl":"10.1007/978-3-031-72069-7_9","url":null,"abstract":"<p><p>Individualized brain parcellations derived from functional MRI (fMRI) are essential for discerning unique functional patterns of individuals, facilitating personalized diagnoses and treatments. Unfortunately, as fMRI signals are inherently noisy, establishing reliable individualized parcellations typically necessitates long-duration fMRI scan (> 25 min), posing a major challenge and resulting in the exclusion of numerous short-duration fMRI scans from individualized studies. To address this issue, we develop a novel Consecutive-Contrastive Spherical U-net (CC-SUnet) to enable the prediction of reliable individualized brain parcellation using short-duration fMRI data, greatly expanding its practical applicability. Specifically, 1) the widely used functional diffusion map (DM), obtained from functional connectivity, is carefully selected as the predictive feature, for its advantage in tracing the transitions between regions while reducing noise. To ensure a robust depiction of brain network, we propose a dual-task model to predict DM and cortical parcellation simultaneously, fully utilizing their reciprocal relationship. 2) By constructing a stepwise dataset to capture the gradual changes of DM over increasing scan durations, a consecutive prediction framework is designed to realize the prediction from short-to-long gradually. 3) A stepwise-denoising-prediction module is further proposed. The noise representations are separated and replaced by the latent representations of a group-level diffusion map, realizing informative guidance and denoising concurrently. 4) Additionally, an N-pair contrastive loss is introduced to strengthen the discriminability of the individualized parcellations. Extensive experimental results demonstrated the superiority of our proposed CC-SUnet in enhancing the reliability of the individualized parcellation with short-duration fMRI data, thereby significantly boosting their utility in individualized studies.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15002 ","pages":"88-98"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12716869/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145807080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient-Specific Real-Time Segmentation in Trackerless Brain Ultrasound.","authors":"Reuben Dorent, Erickson Torio, Nazim Haouchine, Colin Galvin, Sarah Frisken, Alexandra Golby, Tina Kapur, William Wells","doi":"10.1007/978-3-031-72089-5_45","DOIUrl":"10.1007/978-3-031-72089-5_45","url":null,"abstract":"<p><p>Intraoperative ultrasound (iUS) imaging has the potential to improve surgical outcomes in brain surgery. However, its interpretation is challenging, even for expert neurosurgeons. In this work, we designed the first patient-specific framework that performs brain tumor segmentation in trackerless iUS. To disambiguate ultrasound imaging and adapt to the neurosurgeon's surgical objective, a patient-specific real-time network is trained using synthetic ultrasound data generated by simulating virtual iUS sweep acquisitions in pre-operative MR data. Extensive experiments performed in real ultrasound data demonstrate the effectiveness of the proposed approach, allowing for adapting to the surgeon's definition of surgical targets and outperforming non-patient-specific models, neurosurgeon experts, and high-end tracking systems. Our code is available at: https://github.com/ReubenDo/MHVAE-Seg.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15006 ","pages":"477-487"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12714359/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145807089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CryoSAM: Training-free CryoET Tomogram Segmentation with Foundation Models.","authors":"Yizhou Zhao, Hengwei Bian, Michael Mu, Mostofa R Uddin, Zhenyang Li, Xiang Li, Tianyang Wang, Min Xu","doi":"10.1007/978-3-031-72111-3_12","DOIUrl":"10.1007/978-3-031-72111-3_12","url":null,"abstract":"<p><p>Cryogenic Electron Tomography (CryoET) is a useful imaging technology in structural biology that is hindered by its need for manual annotations, especially in particle picking. Recent works have endeavored to remedy this issue with few-shot learning or contrastive learning techniques. However, supervised training is still inevitable for them. We instead choose to leverage the power of existing 2D foundation models and present a novel, training-free framework, CryoSAM. In addition to prompt-based single-particle instance segmentation, our approach can automatically search for similar features, facilitating full tomogram semantic segmentation with only one prompt. CryoSAM is composed of two major parts: 1) a prompt-based 3D segmentation system that uses prompts to complete single-particle instance segmentation recursively with Cross-Plane Self-Prompting, and 2) a Hierarchical Feature Matching mechanism that efficiently matches relevant features with extracted tomogram features. They collaborate to enable the segmentation of all particles of one category with just one particle-specific prompt. Our experiments show that CryoSAM outperforms existing works by a significant margin and requires even fewer annotations in particle picking. Further visualizations demonstrate its ability when dealing with full tomogram segmentation for various subcellular structures. Our code is available at: https://github.com/xulabs/aitom.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15008 ","pages":"124-134"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12923679/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-guided Knowledge-Injected Graph Neural Network for Alzheimer's Diseases.","authors":"Zhepeng Wang, Runxue Bao, Yawen Wu, Guodong Liu, Lei Yang, Liang Zhan, Feng Zheng, Weiwen Jiang, Yanfu Zhang","doi":"10.1007/978-3-031-72069-7_36","DOIUrl":"10.1007/978-3-031-72069-7_36","url":null,"abstract":"<p><p>Graph neural networks (GNNs) are proficient machine learning models in handling irregularly structured data. Nevertheless, their generic formulation falls short when applied to the analysis of brain connectomes in Alzheimer's Disease (AD), necessitating the incorporation of domain-specific knowledge to achieve optimal model performance. The integration of AD-related expertise into GNNs presents a significant challenge. Current methodologies reliant on manual design often demand substantial expertise from external domain specialists to guide the development of novel models, thereby consuming considerable time and resources. To mitigate the need for manual curation, this paper introduces a novel self-guided knowledge-infused multimodal GNN to autonomously integrate domain knowledge into the model development process. We propose to conceptualize existing domain knowledge as natural language, and devise a specialized multimodal GNN framework tailored to leverage this uncurated knowledge to direct the learning of the GNN submodule, thereby enhancing its efficacy and improving prediction interpretability. To assess the effectiveness of our framework, we compile a comprehensive literature dataset comprising recent peer-reviewed publications on AD. By integrating this literature dataset with several real-world AD datasets, our experimental results illustrate the effectiveness of the proposed method in extracting curated knowledge and offering explanations on graphs for domain-specific applications. Furthermore, our approach successfully utilizes the extracted information to enhance the performance of the GNN.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15002 ","pages":"378-388"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11488260/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142484836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gradient Guided Co-Retention Feature Pyramid Network for LDCT Image Denoising.","authors":"Li Zhou, Dayang Wang, Yongshun Xu, Shuo Han, Bahareh Morovati, Shuyi Fan, Hengyong Yu","doi":"10.1007/978-3-031-72390-2_15","DOIUrl":"10.1007/978-3-031-72390-2_15","url":null,"abstract":"<p><p>Low-dose computed tomography (LDCT) reduces the risks of radiation exposure but introduces noise and artifacts into CT images. The Feature Pyramid Network (FPN) is a conventional method for extracting multi-scale feature maps from input images. While upper layers in FPN enhance semantic value, details become generalized with reduced spatial resolution at each layer. In this work, we propose a Gradient Guided Co-Retention Feature Pyramid Network (G2CR-FPN) to address the connection between spatial resolution and semantic value beyond feature maps extracted from LDCT images. The network is structured with three essential paths: the bottom-up path utilizes the FPN structure to generate the hierarchical feature maps, representing multi-scale spatial resolutions and semantic values. Meanwhile, the lateral path serves as a skip connection between feature maps with the same spatial resolution, while also functioning feature maps as directional gradients. This path incorporates a gradient approximation, deriving edge-like enhanced feature maps in horizontal and vertical directions. The top-down path incorporates a proposed co-retention block that learns the high-level semantic value embedded in the preceding map of the path. This learning process is guided by the directional gradient approximation of the high-resolution feature map from the bottom-up path. Experimental results on the clinical CT images demonstrated the promising performance of the model. Our code is available at: https://github.com/liz109/G2CR-FPN.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15012 ","pages":"153-163"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12443485/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145088647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disentangled Hybrid Transformer for Identification of Infants with Prenatal Drug Exposure.","authors":"Jiale Cheng, Zhengwang Wu, Xinrui Yuan, Li Wang, Weili Lin, Karen Grewen, Gang Li","doi":"10.1007/978-3-031-72390-2_7","DOIUrl":"10.1007/978-3-031-72390-2_7","url":null,"abstract":"<p><p>Prenatal drug exposure, which occurs during a time of extraordinary and critical brain development, is typically associated with cognitive, behavioral, and physiological deficits during infancy, childhood, and adolescence. Early identifying infants with prenatal drug exposures and associated biomarkers using neuroimages can help inform earlier, more effective, and personalized interventions to greatly improve later cognitive outcomes. To this end, we propose a novel deep learning model called disentangled hybrid volume-surface transformer for identifying individual infants with prenatal drug exposures. Specifically, we design two distinct branches, a volumetric network for learning non-cortical features in 3D image space, and a surface network for learning features on the highly convoluted cortical surface manifold. To better capture long-range dependency and generate highly discriminative representations, image and surface transformers are respectively employed for the volume and surface branches. Then, a disentanglement strategy is further proposed to separate the representations from two branches into complementary variables and common variables, thus removing redundant information and boosting expressive capability. After that, the disentangled representations are concatenated to a classifier to determine if there is an existence of prenatal drug exposures. We have validated our method on 210 infant MRI scans and demonstrated its superior performance, compared to ablated models and state-of-the-art methods.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15012 ","pages":"67-76"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13004604/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147501339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rethinking Histology Slide Digitization Workflows for Low-Resource Settings.","authors":"Talat Zehra, Joseph Marino, Wendy Wang, Grigoriy Frantsuzov, Saad Nadeem","doi":"10.1007/978-3-031-72083-3_40","DOIUrl":"10.1007/978-3-031-72083-3_40","url":null,"abstract":"<p><p>Histology slide digitization is becoming essential for telepathology (remote consultation), knowledge sharing (education), and using the state-of-the-art artificial intelligence algorithms (augmented/automated end-to-end clinical workflows). However, the cumulative costs of digital multi-slide high-speed brightfield scanners, cloud/on-premises storage, and personnel (IT and technicians) make the current slide digitization workflows out-of-reach for limited-resource settings, further widening the health equity gap; even single-slide manual scanning commercial solutions are costly due to hardware requirements (high-resolution cameras, high-spec PC/workstation, and support for only high-end microscopes). In this work, we present a new cloud slide digitization workflow for creating scanner-quality whole-slide images (WSIs) from uploaded low-quality videos, acquired from cheap and inexpensive microscopes with built-in cameras. Specifically, we present a pipeline to create stitched WSIs while automatically deblurring out-of-focus regions, upsampling input 10X images to 40X resolution, and reducing brightness/contrast and light-source illumination variations. We demonstrate the WSI creation efficacy from our workflow on World Health Organization-declared neglected tropical disease, Cutaneous Leishmaniasis (prevalent only in the poorest regions of the world and only diagnosed by sub-specialist dermatopathologists, rare in poor countries), as well as other common pathologies on core biopsies of breast, liver, duodenum, stomach and lymph node. The code and pretrained models will be accessible via our GitHub (https://github.com/nadeemlab/DeepLIIF), and the cloud platform will be available at https://deepliif.org for uploading microscope videos and downloading/viewing WSIs with shareable links (no sign-in required) for telepathology and knowledge sharing.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15004 ","pages":"427-436"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11786607/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143082977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}