WHO 2016 subtyping and automated segmentation of glioma using multi-task deep learning
Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam（伊拉斯谟医学中心鹿特丹大学医学中心放射科和核医学科生物医学成像组鹿特丹分部）；Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam（伊拉斯谟医学中心鹿特丹大学医学中心放射科和核医学科）；Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam（伊拉斯谟医学中心鹿特丹大学医学中心脑肿瘤中心神经外科）；Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute（伊拉斯谟癌症研究所脑肿瘤中心神经科）；Department of Neurosurgery, Haaglanden Medical Center（海牙 Haaglanden 医疗中心神经外科）；Department of Radiology, Haaglanden Medical Center（海牙 Haaglanden 医疗中心放射科）；Department of Neurosurgery, Cancer Center Amsterdam, Brain Tumor Center, Amsterdam UMC（阿姆斯特丹大学医学中心阿姆斯特丹癌症中心脑肿瘤中心神经外科）
Sebastian R. van der Voort, Fatih Incekara, Maarten M. J. Wijnenga, Georgios Kapsas, Renske Gahrmann, Joost W. Schouten, Rishi Nandoe Tewarie, Geert J. Lycklama… 展开作者
Sebastian R. van der Voort, Fatih Incekara, Maarten M. J. Wijnenga, Georgios Kapsas, Renske Gahrmann, Joost W. Schouten, Rishi Nandoe Tewarie, Geert J. Lycklama, Philip C. De Witt Hamer, Roelant S. Eijgelaar, Pim J. French, Hendrikus J. Dubbink, Arnaud J. P. E. Vincent, Wiro J. Niessen, Martin J. van den Bent, Marion Smits, Stefan Klein
Accurate characterization of glioma is crucial for clinical decision making. A delineation of the tumor is also desirable in the initial decision stages but is a time-consuming task. Leveraging the latest GPU capabilities, we developed a single multi-task convolutional neural network that uses the full 3D, structural, pre-operative MRI scans to can predict the IDH mutation status, the 1p/19q co-deletion status, and the grade of a tumor, while simultaneously segmenting the tumor. We trained our method using the largest, most diverse patient cohort to date containing 1508 glioma patients from 16 institutes. We tested our method on an independent dataset of 240 patients from 13 different institutes, and achieved an IDH-AUC of 0.90, 1p/19q-AUC of 0.85, grade-AUC of 0.81, and a mean whole tumor DICE score of 0.84. Thus, our method non-invasively predicts multiple, clinically relevant parameters and generalizes well to the broader clinical population.
Tencent（腾讯）；Beijing Neurosurgical Institute（北京神经外科研究所）；Capital Medical University（首都医科大学）；Beijing Tiantan Hospital（北京天坛医院）；Sinovation Ventures AI Institute（创新工场人工智能研究院）
Knowledge of molecular subtypes of gliomas can provide valuable information for tailored therapies. This study aimed to investigate the use of deep convolutional neural networks (DCNNs) for noninvasive glioma subtyping with radiological imaging data according to the new taxonomy announced by the World Health Organization in 2016. Methods: A DCNN model was developed for the prediction of the five glioma subtypes based on a hierarchical classification paradigm. This model used three parallel, weight-sharing, deep residual learning networks to process 2.5-dimensional input of trimodal MRI data, i…
Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review
Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所MEVIS）；Hannover Medical School（汉诺威医学院）；Medical Center - University of Freiburg（弗莱堡大学医学中心）；RWTH Aachen University Hospital（亚琛工业大学医院）；Klinikum Bremen-Mitte（不来梅中部医院）；Leibniz Institute for Prevention Research and Epidemiology - BIPS（莱布尼茨预防研究与流行病学研究所）；Bremen Cancer Registry（不来梅癌症登记处）；Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB（弗劳恩霍夫光电、系统技术及图像处理研究所IOSB）；German Heart Center Berlin（柏林德国心脏中心）
University of Leeds（利兹大学）；Leeds Teaching Hospitals NHS Trust（利兹教学医院NHS信托基金会）
Weakly-supervised classification of histopathology slides is a computationally intensive task, with a typical whole slide image (WSI) containing billions of pixels to process. We propose Discriminative Region Active Sampling for Multiple Instance Learning (DRAS-MIL), a computationally efficient slide classification method using attention scores to focus sampling on highly discriminative regions. We apply this to the diagnosis of ovarian cancer histological subtypes, which is an essential part of the patient care pathway as different subtypes have different genetic and molecular profiles, treat…
Faculty of Physics and Mathematics, Benemérita Universidad Autónoma de Puebla（普埃布拉自治大学物理与数学学院）
Gliomas are brain tumors composed of different highly heterogeneous histological subregions. Image analysis techniques to identify relevant tumor substructures have high potential for improving patient diagnosis, treatment and prognosis. However, due to the high heterogeneity of gliomas, the segmentation task is currently a major challenge in the field of medical image analysis. In the present work, the database of the Brain Tumor Segmentation (BraTS) Challenge 2018, composed of multimodal MRI scans of gliomas, was studied. A segmentation methodology based on the design and application of conv…
In the WHO glioma classification guidelines grade, IDH mutation and 1p19q co-deletion play a central role as they are important markers for prognosis and optimal therapy planning. Therefore, we propose a fully automatic, MRI based, 3D pipeline for glioma segmentation and classification. The designed segmentation network was a 3D U-Net achieving an average whole tumor dice score of 90%. After segmentation, the 3D tumor ROI is extracted and fed into the multi-task classification network. The network was trained and evaluated on a large heterogeneous dataset of 628 patients, collected from The Ca…
Deep learning-based classification of breast cancer molecular subtypes from H&E whole-slide images
Uit The Arctic University of Norway（挪威北极大学）；University Hospital of North Norway（北挪威大学医院）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Oslo University Hospital（奥斯陆大学医院）
Transferring Knowledge from High-Quality to Low-Quality MRI for Adult Glioma Diagnosis
The Chinese University of Hong Kong (CUHK)（香港中文大学）；CUHK Shenzhen Research Institute（香港中文大学深圳研究院）；China Medical University（中国医科大学）；University College London（伦敦大学学院）；UCL Hawkes Institute（伦敦大学学院霍克斯研究所）
Kingston General Hospital（金斯顿总医院）；Queen’s University（女王大学）
Primary brain tumors including gliomas continue to pose significant management challenges to clinicians. While the presentation, the pathology, and the clinical course of these lesions are variable, the initial investigations are usually similar. Patients who are suspected to have a brain tumor will be assessed with computed tomography (CT) and magnetic resonance imaging (MRI). The imaging findings are used by neurosurgeons to determine the feasibility of surgical resection and plan such an undertaking. Imaging studies are also an indispensable tool in tracking tumor progression or its respons…
University of Pennsylvania（宾夕法尼亚大学）；Perelman School of Medicine（佩雷尔曼医学院）；COMSATS University Islamabad（COMSATS伊斯兰堡大学）
Cancer histology reveals disease progression and associated molecular processes, and contains rich phenotypic information that is predictive of outcome. In this paper, we developed a computational approach based on deep learning to predict the overall survival and molecular subtypes of glioma patients from microscopic images of tissue biopsies, reflecting measures of microvascular proliferation, mitotic activity, nuclear atypia, and the presence of necrosis. Whole-slide images from 663 unique patients [IDH: 333 IDH-wildtype, 330 IDH-mutants, 1p/19q: 201 1p/19q non-codeleted, 129 1p/19q codelet…
Adult Glioma Segmentation in Sub-Saharan Africa using Transfer Learning on Stratified Finetuning Data
Sheikh Zayed Institute for Pediatric Surgical Innovation（谢赫·扎耶德儿科外科创新研究所）；Children’s National Hospital（儿童国家医院）；Biomedical Image Technologies（生物医学影像技术中心）；Universidad Politécnica de Madrid（马德里理工大学）；CIBER-BBN（西班牙生物医学网络研究中心）；ISCIII（卡洛斯三世卫生研究所）；School of Medicine and Health Sciences, George Washington University（乔治·华盛顿大学医学与健康科学学院）
Training Beyond Convergence: Grokking nnU-Net for Glioma Segmentation in Sub-Saharan MRI
Multimedia University（多媒体大学）；University of British Columbia（不列颠哥伦比亚大学）；McGill University（麦吉尔大学）；Montreal Neurological Institute（蒙特利尔神经学研究所）；Federal University of Technology Minna（明纳联邦理工大学）；Medical Artificial Intelligence Laboratory（医学人工智能实验室）；Friedrich-Alexander-Universität（弗里德里希-亚历山大大学）
National University of Singapore（新加坡国立大学）；National Institute of Technology, Tiruchirappalli（蒂鲁吉拉伯利国立理工学院）
Segmentation of brain tumor from magnetic resonance imaging (MRI) is a vital process to improve diagnosis, treatment planning and to study the difference between subjects with tumor and healthy subjects. In this paper, we exploit a convolutional neural network (CNN) with hypercolumn technique to segment tumor from healthy brain tissue. Hypercolumn is the concatenation of a set of vectors which form by extracting convolutional features from multiple layers. Proposed model integrates batch normalization (BN) approach with hypercolumn. BN layers help to alleviate the internal covariate shift duri…
Reutlingen University（罗伊特林根大学）；Menoufia University（梅努菲亚大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
Automatic segmentation is essential for the brain tumor diagnosis, disease prognosis, and follow-up therapy of patients with gliomas. Still, accurate detection of gliomas and their sub-regions in multimodal MRI is very challenging due to the variety of scanners and imaging protocols. Over the last years, the BraTS Challenge has provided a large number of multi-institutional MRI scans as a benchmark for glioma segmentation algorithms. This paper describes our contribution to the BraTS 2022 Continuous Evaluation challenge. We propose a new ensemble of multiple deep learning frameworks namely, De…
Shenzhen University（深圳大学）；Islamic Azad University（伊斯兰阿扎德大学）
Magnetic resonance imaging (MRI) provides varying tissue contrast images of internal organs based on a strong magnetic field. Despite the non-invasive advantage of MRI in frequent imaging, the low contrast MR images in the target area make tissue segmentation a challenging problem. This paper demonstrates the potential benefits of image-to-image translation techniques to generate synthetic high tissue contrast (HTC) images. Notably, we adopt a new cycle generative adversarial network (CycleGAN) with an attention mechanism to increase the contrast within underlying tissues. The attention block,…
Glioma, the malignant brain tumor, requires immediate treatment to improve the survival of patients. Gliomas heterogeneous nature makes the segmentation difficult, especially for sub-regions like necrosis, enhancing tumor, non-enhancing tumor, and Edema. Deep neural networks like full convolution neural networks and ensemble of fully convolution neural networks are successful for Glioma segmentation. The paper demonstrates the use of a 3D fully convolution neural network with a three layer encoder decoder approach for layer arrangement. The encoder blocks include the dense modules, and decoder…
A vision transformer-based framework for knowledge transfer from multi-modal to mono-modal lymphoma subtyping models
Junia（朱尼亚高等工程学院）；University of Lille（里尔大学）；CNRS（法国国家科学研究中心）；Centrale Lille（里尔中央理工学院）；IMT Nord Europe（北欧电信高等学院）；Institut Mines-Télécom（法国国立矿业与电信学院）；University Hospital of Lille（里尔大学医院）；The University of Tokyo（东京大学）；Yncrea Méditerranée（Yncrea地中海分部）；Yncrea Ouest（Yncrea西部分部）
Beijing Technology and Business University（北京工商大学）
Automatic segmentation of glioma and its subregions is of great significance for diagnosis, treatment and monitoring of disease. In this paper, an augmentation method, called TensorMixup, was proposed and applied to the three dimensional U-Net architecture for brain tumor segmentation. The main ideas included that first, two image patches with size of 128 in three dimensions were selected according to glioma information of ground truth labels from the magnetic resonance imaging data of any two patients with the same modality. Next, a tensor in which all elements were independently sampled from…
Multi-Resolution Histopathology Patch Graphs for Ovarian Cancer Subtyping
Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB)（生物医学计算成像与模拟技术中心）；School of Computing, University of Leeds（利兹大学计算机学院）；Leeds Institute of Medical Research at St James’s, School of Medicine, University of Leeds（利兹大学医学院圣詹姆斯利兹医学研究所）；Leeds Cancer Centre, St James’s University Hospital（圣詹姆斯大学医院利兹癌症中心）
How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation
University of Minho（米尼奥大学）；Institute for Artificial Intelligence in Medicine（人工智能医学研究所）；Graz University of Technology（格拉茨工业大学）；University Medicine Essen（埃森大学医学中心）；Cancer Research Center Cologne Essen (CCCE)（科隆-埃森癌症研究中心）；University Hospital Essen（埃森大学医院）；German Cancer Consortium (DKTK)（德国癌症联盟）
Hôpital Erasme（埃拉斯姆医院）；Université libre de Bruxelles（布鲁塞尔自由大学）；Center for Microscopy and Molecular Imaging（显微与分子成像中心）
Diffuse gliomas are highly infiltrative tumors whose early diagnosis and follow-up usually rely on magnetic resonance imaging (MRI). However, the limited sensitivity of this technique makes it impossible to directly assess the extent of the glioma cell invasion, leading to sub-optimal treatment planing. Reaction-diffusion growth models have been proposed for decades to extrapolate glioma cell infiltration beyond margins visible on MRI and predict its spatial-temporal evolution. These models nevertheless require an initial condition, that is the tumor cell density values at every location of th…
Henry Ford Health System（亨利福特医疗系统）；Wayne State University（韦恩州立大学）
Every year thousands of patients are diagnosed with a glioma, a type of malignant brain tumor. Physicians use MR images as a key tool in the diagnosis and treatment of these patients. Neural networks show great potential to aid physicians in the medical image analysis. This study investigates the use of varying amounts of synthetic brain T1-weighted (T1), post-contrast T1-weighted (T1Gd), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (FLAIR) MR images created by a generative adversarial network to overcome the lack of annotated medical image data in training separate 2D U-Nets t…
Hôpital Erasme（埃拉斯姆医院）；Université libre de Bruxelles（布鲁塞尔自由大学）
Reaction-diffusion models have been proposed for decades to capture the growth of gliomas, the most common primary brain tumours. However, severe limitations regarding the estimation of the initial conditions and parameter values of such models have restrained their clinical use as a personalised tool. In this work, we investigate the ability of deep convolutional neural networks (DCNNs) to address the pitfalls commonly encountered in the field. Based on 1,200 synthetic tumours grown over real brain geometries derived from magnetic resonance (MR) data of 6 healthy subjects, we demonstrate the …
In the past few years, deep learning (DL) models have drawn great attention and shown superior performance on brain tumor and subregion segmentation tasks. However, the success is limited to segmentation of adult gliomas, where sufficient data have been collected, manually labeled, and published for training DL models. It is still challenging to segment pediatric tumors, because the appearances are different from adult gliomas. Hence, directly applying a pretained DL model on pediatric data usually generates unacceptable results. Because pediatric data is very limited, both labeled and unlabel…
Quantifying U-Net Uncertainty in Multi-Parametric MRI-based Glioma Segmentation by Spherical Image Projection
Duke University（杜克大学）；Duke Kunshan University（昆山杜克大学）；The University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）；The University of Texas MD Anderson Graduate School of Biomedical Science（德克萨斯大学MD安德森生物医学研究生院）
Duke University（杜克大学）；Duke Kunshan University（昆山杜克大学）；North Carolina State University（北卡罗来纳州立大学）
Purpose: To develop a neural ordinary differential equation (ODE) model for visualizing deep neural network (DNN) behavior during multi-parametric MRI (mp-MRI) based glioma segmentation as a method to enhance deep learning explainability. Methods: By hypothesizing that deep feature extraction can be modeled as a spatiotemporally continuous process, we designed a novel deep learning model, neural ODE, in which deep feature extraction was governed by an ODE without explicit expression. The dynamics of 1) MR images after interactions with DNN and 2) segmentation formation can be visualized after …
Massachusetts General Hospital（麻省总医院）；Harvard University（哈佛大学）；General Electric Healthcare（通用电气医疗集团）；Indian Institute of Science（印度科学学院）
Gliomas are one of the most frequent brain tumors and are classified into high grade and low grade gliomas. The segmentation of various regions such as tumor core, enhancing tumor etc. plays an important role in determining severity and prognosis. Here, we have developed a multi-threshold model based on attention U-Net for identification of various regions of the tumor in magnetic resonance imaging (MRI). We propose a multi-path segmentation and built three separate models for the different regions of interest. The proposed model achieved mean Dice Coefficient of 0.59, 0.72, and 0.61 for enhan…
University of Tehran（德黑兰大学）；University of Nottingham（诺丁汉大学）
In this study, an automated three dimensional (3D) deep segmentation approach for detecting gliomas in 3D pre-operative MRI scans is proposed. Then, a classi-fication algorithm based on random forests, for survival prediction is presented. The objective is to segment the glioma area and produce segmentation labels for its different sub-regions, i.e. necrotic and the non-enhancing tumor core, the peri-tumoral edema, and enhancing tumor. The proposed deep architecture for the segmentation task encompasses two parallel streamlines with two different reso-lutions. One deep convolutional neural net…
A Simple yet Effective Framework for Pre-treatment and Post-treatment Glioma Segmentation Using Multimodal MRI
School of Statistics, East China Normal University（华东师范大学统计学院）；Department of Biostatistics, School of Global Public Health, New York University（纽约大学全球公共卫生学院生物统计学系）
Glioma is one of the most common types of brain tumors; it arises in the glial cells in the human brain and in the spinal cord. In addition to having a high mortality rate, glioma treatment is also very expensive. Hence, automatic and accurate segmentation and measurement from the early stages are critical in order to prolong the survival rates of the patients and to reduce the costs of the treatment. In the present work, we propose a novel end-to-end cascaded network for semantic segmentation that utilizes the hierarchical structure of the tumor sub-regions with ResNet-like blocks and Squeeze…
Multi-Beholder: Biomarker Prediction for Low-Grade Glioma with Multiple Instance Learning and One-Class Classification
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院，清华大学）；Department of Oncology, Xiangya Hospital, Central South University（湘雅医院肿瘤科，中南大学）；School of Science, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）科学学院）；Department of Neurosurgery, Xiangya Hospital, Central South University（湘雅医院神经外科，中南大学）；School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）计算机科学与技术学院）
Gliomas are among the most aggressive and deadly brain tumors. This paper details the proposed Deep Neural Network architecture for brain tumor segmentation from Magnetic Resonance Images. The architecture consists of a cascade of three Deep Layer Aggregation neural networks, where each stage elaborates the response using the feature maps and the probabilities of the previous stage, and the MRI channels as inputs. The neuroimaging data are part of the publicly available Brain Tumor Segmentation (BraTS) 2020 challenge dataset, where we evaluated our proposal in the BraTS 2020 Validation and Tes…
A New Logic For Pediatric Brain Tumor Segmentation
1 Northwestern University, Feinberg School of Medicine, Chicago IL, USA 2 Children’s National Hospital, Sheikh Zayed Institute for Pediatric Surgical Innovation, Washington, DC, USA 3 Ann \& Robert H Lurie Children's Hospital of Chicago, Chicago, IL, USA
Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-oncology (I3CR-WANO)
Washington University in St. Louis（圣路易斯华盛顿大学）；Washington University School of Medicine（华盛顿大学医学院）；Mallinckrodt Institute of Radiology（马林克罗特放射学研究所）；The University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）
Rowan University（罗文大学）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）
Magnetic resonance imaging (MRI) is routinely used for brain tumor diagnosis, treatment planning, and post-treatment surveillance. Recently, various models based on deep neural networks have been proposed for the pixel-level segmentation of tumors in brain MRIs. However, the structural variations, spatial dissimilarities, and intensity inhomogeneity in MRIs make segmentation a challenging task. We propose a new end-to-end brain tumor segmentation architecture based on U-Net that integrates Inception modules and dilated convolutions into its contracting and expanding paths. This allows us to ex…
Indian Institute of Technology Madras（印度理工学院马德拉斯分校）；Intel Technology India Pvt. Ltd（英特尔技术印度私人有限公司）
We utilize 3-D fully convolutional neural networks (CNN) to segment gliomas and its constituents from multimodal Magnetic Resonance Images (MRI). The architecture uses dense connectivity patterns to reduce the number of weights and residual connections and is initialized with weights obtained from training this model with BraTS 2018 dataset. Hard mining is done during training to train for the difficult cases of segmentation tasks by increasing the dice similarity coefficient (DSC) threshold to choose the hard cases as epoch increases. On the BraTS2020 validation data (n = 125), this architect…
The Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planning
Universitätsklinikum Erlangen（埃尔兰根大学医院）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希·亚历山大大学）；Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN)（埃尔兰根-欧洲分子神经科学网络综合癌症中心）
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histological sub-regions, i.e., peritumoral edema, necrotic core, enhancing and non-enhancing tumour core. Although brain tumours can easily be detected using multi-modal MRI, accurate tumor segmentation is a challenging task. Hence, using the data provided by the BraTS Challenge 2020, we propose a 3D volume-to-volume Generative Adversarial Network for segmentation of brain tumours. The model, called Vox2Vox, generates realistic segmentation outputs fro…
Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）
Multi-modal magnetic resonance (MR) imaging provides great potential for diagnosing and analyzing brain gliomas. In clinical scenarios, common MR sequences such as T1, T2 and FLAIR can be obtained simultaneously in a single scanning process. However, acquiring contrast enhanced modalities such as T1ce requires additional time, cost, and injection of contrast agent. As such, it is clinically meaningful to develop a method to synthesize unavailable modalities which can also be used as additional inputs to downstream tasks (e.g., brain tumor segmentation) for performance enhancing. In this work, …
The paper demonstrates the use of the fully convolutional neural network for glioma segmentation on the BraTS 2019 dataset. Three-layers deep encoder-decoder architecture is used along with dense connection at encoder part to propagate the information from coarse layer to deep layers. This architecture is used to train three tumor sub-components separately. Subcomponent training weights are initialized with whole tumor weights to get the localization of the tumor within the brain. At the end, three segmentation results were merged to get the entire tumor segmentation. Dice Similarity of traini…
College of Physics and Information Engineering, Fuzhou University（福州大学物理与信息工程学院）；School of Data Science, Fudan University（复旦大学数据科学学院）；School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；School of Biomedical Engineering and Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与成像科学学院）
Delineating the brain tumor from magnetic resonance (MR) images is critical for the treatment of gliomas. However, automatic delineation is challenging due to the complex appearance and ambiguous outlines of tumors. Considering that multi-modal MR images can reflect different tumor biological properties, we develop a novel multi-modal tumor segmentation network (MMTSN) to robustly segment brain tumors based on multi-modal MR images. The MMTSN is composed of three sub-branches and a main branch. Specifically, the sub-branches are used to capture different tumor features from multi-modal images,…
Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings
University of Ibadan（伊巴丹大学）；African Institute for Mathematical Sciences（非洲数学科学研究所）；University of Cape Town（开普敦大学）；South African National Bioinformatics Institute（南非国家生物信息学研究所）；University of Western Cape（西开普大学）；Carnegie Mellon University（卡内基梅隆大学）；McGill University（麦吉尔大学）；Lawson Health Research Institute（劳森健康研究所）；University of British Columbia（不列颠哥伦比亚大学）
Background: Clear cell renal cell carcinoma (ccRCC) is the most common renal-related tumor with high heterogeneity. There is still an urgent need for novel diagnostic and prognostic biomarkers for ccRCC. Methods: We proposed a weakly-supervised deep learning strategy using conventional histology of 1752 whole slide images from multiple centers. Our study was demonstrated through internal cross-validation and external validations for the deep learning-based models. Results: Automatic diagnosis for ccRCC through intelligent subtyping of renal cell carcinoma was proved in this study. Our graderis…
