Search Results for author:
Found 105 papers, 21 papers with code
Meta-information-aware Dual-path Transformer for Differential Diagnosis of Multi-type Pancreatic Lesions in Multi-phase CT
Accurate detection, segmentation, and differential diagnosis of the full taxonomy of pancreatic lesions, i. e., normal, seven major types of lesions, and other lesions, is critical to aid the clinical decision-making of patient management and treatment.
Anatomical Invariance Modeling and Semantic Alignment for Self-supervised Learning in 3D Medical Image Segmentation
Self-supervised learning (SSL) has recently achieved promising performance for 3D medical image segmentation tasks.
Continual Segment: Towards a Single, Unified and Accessible Continual Segmentation Model of 143 Whole-body Organs in CT Scans
Deep learning empowers the mainstream medical image segmentation methods.
Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans
A certain number of AI models need to be assembled non-trivially to match the diagnostic process of a human reading a CT scan.
A deep local attention network for pre-operative lymph node metastasis prediction in pancreatic cancer via multiphase CT imaging
Lymph node (LN) metastasis status is one of the most critical prognostic and cancer staging factors for patients with resectable pancreatic ductal adenocarcinoma (PDAC), or in general, for any types of solid malignant tumors.
Med-Query: Steerable Parsing of 9-DoF Medical Anatomies with Query Embedding
For rib parsing, CT scans have been annotated at the rib instance-level for quantitative evaluation, similarly for spine vertebrae and abdominal organs.
A New Probabilistic V-Net Model with Hierarchical Spatial Feature Transform for Efficient Abdominal Multi-Organ Segmentation
Accurate and robust abdominal multi-organ segmentation from CT imaging of different modalities is a challenging task due to complex inter- and intra-organ shape and appearance variations among abdominal organs.
LViT: Language meets Vision Transformer in Medical Image Segmentation
In our model, medical text annotation is introduced to compensate for the quality deficiency in image data.
Ranked #1 on
Medical Image Segmentation
on MoNuSeg
Localized Adversarial Domain Generalization
Adversarial domain generalization is a popular approach to DG, but conventional approaches (1) struggle to sufficiently align features so that local neighborhoods are mixed across domains; and (2) can suffer from feature space over collapse which can threaten generalization performance.
Lumbar Bone Mineral Density Estimation from Chest X-ray Images: Anatomy-aware Attentive Multi-ROI Modeling
Osteoporosis is a common chronic metabolic bone disease often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, e. g. via Dual-energy X-ray Absorptiometry (DXA).
Coherence Learning using Keypoint-based Pooling Network for Accurately Assessing Radiographic Knee Osteoarthritis
In this work, we propose a computer-aided diagnosis approach to provide more accurate and consistent assessments of both composite and fine-grained OA grades simultaneously.
Comprehensive and Clinically Accurate Head and Neck Organs at Risk Delineation via Stratified Deep Learning: A Large-scale Multi-Institutional Study
Accurate organ at risk (OAR) segmentation is critical to reduce the radiotherapy post-treatment complications.
Circle Representation for Medical Object Detection
Compared with the conventional bounding box representation, the proposed bounding circle representation innovates in three-fold: (1) it is optimized for ball-shaped biomedical objects; (2) The circle representation reduced the degree of freedom compared with box representation; (3) It is naturally more rotation invariant.
Ranked #1 on
Medical Object Detection
on MoNuSeg 2018
A deep learning pipeline for localization, differentiation, and uncertainty estimation of liver lesions using multi-phasic and multi-sequence MRI
Results: the proposed CAD solution achieves a mean F1 score of 0. 62, outperforming the abdominal radiologist (0. 47), matching the junior hepatology radiologist (0. 61), and underperforming the senior hepatology radiologist (0. 68).
Accurate and Generalizable Quantitative Scoring of Liver Steatosis from Ultrasound Images via Scalable Deep Learning
High diagnostic performance was observed across all viewpoints: area under the curves of the ROC to classify >=mild, >=moderate, =severe steatosis grades were 0. 85, 0. 90, and 0. 93, respectively.
Multi-institutional Validation of Two-Streamed Deep Learning Method for Automated Delineation of Esophageal Gross Tumor Volume using planning-CT and FDG-PETCT
252 institution-1 patients had a treatment planning-CT (pCT) and a pair of diagnostic FDG-PETCT; 354 patients from other 3 institutions had only pCT.
SAME: Deformable Image Registration based on Self-supervised Anatomical Embeddings
In this work, we introduce a fast and accurate method for unsupervised 3D medical image registration.
DeepStationing: Thoracic Lymph Node Station Parsing in CT Scans using Anatomical Context Encoding and Key Organ Auto-Search
Lymph node station (LNS) delineation from computed tomography (CT) scans is an indispensable step in radiation oncology workflow.
A Flexible Three-Dimensional Hetero-phase Computed Tomography Hepatocellular Carcinoma (HCC) Detection Algorithm for Generalizable and Practical HCC Screening
We develop a flexible three-dimensional deep algorithm, called hetero-phase volumetric detection (HPVD), that can accept any combination of contrast-phase inputs and with adjustable sensitivity depending on the clinical purpose.
Lesion Segmentation and RECIST Diameter Prediction via Click-driven Attention and Dual-path Connection
PDNet learns comprehensive and representative deep image features for our tasks and produces more accurate results on both lesion segmentation and RECIST diameter prediction.
Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss
Accurately segmenting a variety of clinically significant lesions from whole body computed tomography (CT) scans is a critical task on precision oncology imaging, denoted as universal lesion segmentation (ULS).
Scalable Semi-supervised Landmark Localization for X-ray Images using Few-shot Deep Adaptive Graph
It first trains a DAG model on the labeled data and then fine-tunes the pre-trained model on the unlabeled data with a teacher-student SSL mechanism.
Learning from Subjective Ratings Using Auto-Decoded Deep Latent Embeddings
Importantly, ADDLE does not expect multiple raters per image in training, meaning it can readily learn from data mined from hospital archives.
Deep Implicit Statistical Shape Models for 3D Medical Image Delineation
DISSMs use a deep implicit surface representation to produce a compact and descriptive shape latent space that permits statistical models of anatomical variance.
Opportunistic Screening of Osteoporosis Using Plain Film Chest X-ray
In this paper, we propose a method to predict BMD from Chest X-ray (CXR), one of the most common, accessible, and low-cost medical image examinations.
Semi-Supervised Learning for Bone Mineral Density Estimation in Hip X-ray Images
Specifically, we propose a new semi-supervised self-training algorithm to train the BMD regression model using images coupled with DEXA measured BMDs and unlabeled images with pseudo BMDs.
Hetero-Modal Learning and Expansive Consistency Constraints for Semi-Supervised Detection from Multi-Sequence Data
Lesion detection serves a critical role in early diagnosis and has been well explored in recent years due to methodological advancesand increased data availability.
Sequential Learning on Liver Tumor Boundary Semantics and Prognostic Biomarker Mining
(2) The sampled deep vertex features with positional embedding are mapped into a sequential space and decoded by a multilayer perceptron (MLP) for semantic classification.
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning.
Ranked #3 on
Medical Image Segmentation
on ACDC
Knowledge Distillation with Adaptive Asymmetric Label Sharpening for Semi-supervised Fracture Detection in Chest X-rays
Exploiting available medical records to train high performance computer-aided diagnosis (CAD) models via the semi-supervised learning (SSL) setting is emerging to tackle the prohibitively high labor costs involved in large-scale medical image annotations.
Automatic Vertebra Localization and Identification in CT by Spine Rectification and Anatomically-constrained Optimization
This paper proposes a robust and accurate method that effectively exploits the anatomical knowledge of the spine to facilitate vertebra localization and identification.
Fully-Automated Liver Tumor Localization and Characterization from Multi-Phase MR Volumes Using Key-Slice ROI Parsing: A Physician-Inspired Approach
Using radiological scans to identify liver tumors is crucial for proper patient treatment.
Interactive Radiotherapy Target Delineation with 3D-Fused Context Propagation
The proposed method is evaluated on two published radiotherapy target contouring datasets of nasopharyngeal and esophageal cancer.
Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies
In this work, we present deep lesion tracker (DLT), a deep learning approach that uses both appearance- and anatomical-based signals.
3D Graph Anatomy Geometry-Integrated Network for Pancreatic Mass Segmentation, Diagnosis, and Quantitative Patient Management
We exploit the feasibility to distinguish pancreatic ductal adenocarcinoma (PDAC) from the nine other nonPDAC masses using multi-phase CT imaging.
A New Window Loss Function for Bone Fracture Detection and Localization in X-ray Images with Point-based Annotation
Object detection methods are widely adopted for computer-aided diagnosis using medical images.
SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images
We introduce such an approach, called Self-supervised Anatomical eMbedding (SAM).
Contour Transformer Network for One-shot Segmentation of Anatomical Structures
In this work, we present Contour Transformer Network (CTN), a one-shot anatomy segmentation method with a naturally built-in human-in-the-loop mechanism.
Learning from Multiple Datasets with Heterogeneous and Partial Labels for Universal Lesion Detection in CT
For example, DeepLesion is such a large-scale CT image dataset with lesions of various types, but it also has many unlabeled lesions (missing annotations).
User-Guided Domain Adaptation for Rapid Annotation from User Interactions: A Study on Pathological Liver Segmentation
Mask-based annotation of medical images, especially for 3D data, is a bottleneck in developing reliable machine learning models.
Deep Volumetric Universal Lesion Detection using Light-Weight Pseudo 3D Convolution and Surface Point Regression
Identifying, measuring and reporting lesions accurately and comprehensively from patient CT scans are important yet time-consuming procedures for physicians.
Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network
Specifically, we first utilize a 3D convolutional neural network with ROI-pooling to extract the GTV$_{LN}$'s instance-wise appearance features.
Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-based Gating using 3D CT/PET Imaging in Radiotherapy
Finding, identifying and segmenting suspicious cancer metastasized lymph nodes from 3D multi-modality imaging is a clinical task of paramount importance.
DeepPrognosis: Preoperative Prediction of Pancreatic Cancer Survival and Surgical Margin via Contrast-Enhanced CT Imaging
We present a multi-task CNN to accomplish both tasks of outcome and margin prediction where the network benefits from learning the tumor resection margin related features to improve survival prediction.
Robust Pancreatic Ductal Adenocarcinoma Segmentation with Multi-Institutional Multi-Phase Partially-Annotated CT Scans
A student model is trained on both manual and pseudo annotated multi-phase images.
Reliable Liver Fibrosis Assessment from Ultrasound using Global Hetero-Image Fusion and View-Specific Parameterization
Ultrasound (US) is a critical modality for diagnosing liver fibrosis.
Faster Mean-shift: GPU-accelerated clustering for cosine embedding-based cell segmentation and tracking
With both embedding simulation and empirical validation via the four cohorts from the ISBI cell tracking challenge, the proposed Faster Mean-shift algorithm achieved 7-10 times speedup compared to the state-of-the-art embedding based cell instance segmentation and tracking algorithm.
Learning to Segment Anatomical Structures Accurately from One Exemplar
Accurate segmentation of critical anatomical structures is at the core of medical image analysis.
Anatomy-Aware Siamese Network: Exploiting Semantic Asymmetry for Accurate Pelvic Fracture Detection in X-ray Images
A new contrastive feature learning component in our Siamese network is designed to optimize the deep image features being more salient corresponding to the underlying semantic asymmetries (caused by pelvic fracture occurrences).
Harvesting, Detecting, and Characterizing Liver Lesions from Large-scale Multi-phase CT Data via Deep Dynamic Texture Learning
To this end, we propose a fully-automated and multi-stage liver tumor characterization framework designed for dynamic contrast computed tomography (CT).
CircleNet: Anchor-free Detection with Circle Representation
In this work, we propose CircleNet, a simple anchor-free detection method with circle representation for detection of the ball-shaped glomerulus.
Universal Lesion Detection by Learning from Multiple Heterogeneously Labeled Datasets
First, we learn a multi-head multi-task lesion detector using all datasets and generate lesion proposals on DeepLesion.
Ranked #5 on
Medical Object Detection
on DeepLesion
(using extra training data)
Co-Heterogeneous and Adaptive Segmentation from Multi-Source and Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation
In medical imaging, organ/pathology segmentation models trained on current publicly available and fully-annotated datasets usually do not well-represent the heterogeneous modalities, phases, pathologies, and clinical scenarios encountered in real environments.
Detecting Scatteredly-Distributed, Small, andCritically Important Objects in 3D OncologyImaging via Decision Stratification
We focus on the detection and segmentation of oncology-significant (or suspicious cancer metastasized) lymph nodes (OSLNs), which has not been studied before as a computational task.
JSSR: A Joint Synthesis, Segmentation, and Registration System for 3D Multi-Modal Image Alignment of Large-scale Pathological CT Scans
We extensively evaluate our JSSR system on a large-scale medical image dataset containing 1, 485 patient CT imaging studies of four different phases (i. e., 5, 940 3D CT scans with pathological livers) on the registration, segmentation and synthesis tasks.
Structured Landmark Detection via Topology-Adapting Deep Graph Learning
Image landmark detection aims to automatically identify the locations of predefined fiducial points.
Ranked #4 on
Face Alignment
on COFW-68
Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search
This is the goal of our work, where we introduce stratified organ at risk segmentation (SOARS), an approach that stratifies OARs into anchor, mid-level, and small & hard (S&H) categories.
Unsupervised Learning of Landmarks based on Inter-Intra Subject Consistencies
To recover from the transformed images back to the original subject, the landmark detector is forced to learn spatial locations that contain the consistent semantic meanings both for the paired intra-subject images and between the paired inter-subject images.
Lesion Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scale
This is the goal of our work, where we develop a powerful system to harvest missing lesions from the DeepLesion dataset at high precision.
CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT
This is the focus of our work, where we present a principled data curation tool to extract multi-phase CT liver studies and identify each scan's phase from a real-world and heterogenous hospital PACS dataset.
Accurate Esophageal Gross Tumor Volume Segmentation in PET/CT using Two-Stream Chained 3D Deep Network Fusion
Gross tumor volume (GTV) segmentation is a critical step in esophageal cancer radiotherapy treatment planning.
Deep Esophageal Clinical Target Volume Delineation using Encoded 3D Spatial Context of Tumors, Lymph Nodes, and Organs At Risk
Clinical target volume (CTV) delineation from radiotherapy computed tomography (RTCT) images is used to define the treatment areas containing the gross tumor volume (GTV) and/or sub-clinical malignant disease for radiotherapy (RT).
Weakly Supervised Universal Fracture Detection in Pelvic X-rays
In this paper, we propose a two-stage hip and pelvic fracture detection method that executes localized fracture classification using weakly supervised ROI mining.
Radiotherapy Target Contouring with Convolutional Gated Graph Neural Network
Instead, inspired by the treating methodology of considering meaningful information across slices, we used Gated Graph Neural Network to frame this problem more efficiently.
Spatio-Temporal Convolutional LSTMs for Tumor Growth Prediction by Learning 4D Longitudinal Patient Data
Results validate that the ST-ConvLSTM produces a Dice score of 83. 2%+-5. 1% and a RVD of 11. 2%+-10. 8%, both significantly outperforming (p<0. 05) other compared methods of linear model, ConvLSTM, and generative adversarial network (GAN) under the metric of predicting future tumor volumes.
Attention-Guided Curriculum Learning for Weakly Supervised Classification and Localization of Thoracic Diseases on Chest Radiographs
In addition, highly confident samples (measured by classification probabilities) and their corresponding class-conditional heatmaps (generated by the CNN) are extracted and further fed into the AGCL framework to guide the learning of more distinctive convolutional features in the next iteration.
CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement
The first GAN reduces the noise in the CT image and the second GAN generates a higher resolution image with enhanced boundaries and high contrast.
Iterative Attention Mining for Weakly Supervised Thoracic Disease Pattern Localization in Chest X-Rays
Given image labels as the only supervisory signal, we focus on harvesting, or mining, thoracic disease localizations from chest X-ray images.
Accurate Weakly-Supervised Deep Lesion Segmentation using Large-Scale Clinical Annotations: Slice-Propagated 3D Mask Generation from 2D RECIST
Volumetric lesion segmentation from computed tomography (CT) images is a powerful means to precisely assess multiple time-point lesion/tumor changes.
Pancreas Segmentation in CT and MRI Images via Domain Specific Network Designing and Recurrent Neural Contextual Learning
Automatic pancreas segmentation in radiology images, eg., computed tomography (CT) and magnetic resonance imaging (MRI), is frequently required by computer-aided screening, diagnosis, and quantitative assessment.
Deep LOGISMOS: Deep Learning Graph-based 3D Segmentation of Pancreatic Tumors on CT scans
The cost for each node of the graph is determined by the UNet probability maps.
Convolutional Invasion and Expansion Networks for Tumor Growth Prediction
Tumor growth is associated with cell invasion and mass-effect, which are traditionally formulated by mathematical models, namely reaction-diffusion equations and biomechanics.
Self-Learning to Detect and Segment Cysts in Lung CT Images without Manual Annotation
In recent years, deep neural networks achieve impressive performances on many medical image segmentation tasks by supervised learning on large manually annotated data.
Accurate Weakly Supervised Deep Lesion Segmentation on CT Scans: Self-Paced 3D Mask Generation from RECIST
Toward this end, we introduce a convolutional neural network based weakly supervised self-paced segmentation (WSSS) method to 1) generate the initial lesion segmentation on the axial RECIST-slice; 2) learn the data distribution on RECIST-slices; 3) adapt to segment the whole volume slice by slice to finally obtain a volumetric segmentation.
DeepPap: Deep Convolutional Networks for Cervical Cell Classification
However, the success of most traditional classification methods relies on the presence of accurate cell segmentations.
TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-rays
Chest X-rays are one of the most common radiological examinations in daily clinical routines.
NegBio: a high-performance tool for negation and uncertainty detection in radiology reports
Negative and uncertain medical findings are frequent in radiology reports, but discriminating them from positive findings remains challenging for information extraction.
Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database
Then, a triplet network is utilized to learn lesion embeddings with a sequential sampling strategy to depict their hierarchical similarity structure.
DeepLesion: Automated Deep Mining, Categorization and Detection of Significant Radiology Image Findings using Large-Scale Clinical Lesion Annotations
We categorize the collection of lesions using an unsupervised deep mining scheme to generate clustered pseudo lesion labels.
Improving Deep Pancreas Segmentation in CT and MRI Images via Recurrent Neural Contextual Learning and Direct Loss Function
The output layer of this network module is then connected to recurrent layers and can be fine-tuned for contextual learning, in an end-to-end manner.
Unsupervised Body Part Regression via Spatially Self-ordering Convolutional Neural Networks
In this paper, we propose a convolutional neural network (CNN) based Unsupervised Body part Regression (UBR) algorithm to address this problem.
Progressive and Multi-Path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images
Pathological lung segmentation (PLS) is an important, yet challenging, medical image application due to the wide variability of pathological lung appearance and shape.
Personalized Pancreatic Tumor Growth Prediction via Group Learning
Our predictive model is pretrained on a group data set and personalized on the target patient data to estimate the future spatio-temporal progression of the patient's tumor.
ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosis of many lung diseases.
Spatial Aggregation of Holistically-Nested Convolutional Neural Networks for Automated Pancreas Localization and Segmentation
Accurate and automatic organ segmentation from 3D radiological scans is an important yet challenging problem for medical image analysis.
Ranked #1 on
3D Medical Imaging Segmentation
on TCIA Pancreas-CT
Unsupervised Joint Mining of Deep Features and Image Labels for Large-scale Radiology Image Categorization and Scene Recognition
The recent rapid and tremendous success of deep convolutional neural networks (CNN) on many challenging computer vision tasks largely derives from the accessibility of the well-annotated ImageNet and PASCAL VOC datasets.
Holistic Interstitial Lung Disease Detection using Deep Convolutional Neural Networks: Multi-label Learning and Unordered Pooling
Accurately predicting and detecting interstitial lung disease (ILD) patterns given any computed tomography (CT) slice without any pre-processing prerequisites, such as manually delineated regions of interest (ROIs), is a clinically desirable, yet challenging goal.
Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation
Accurate automatic organ segmentation is an important yet challenging problem for medical image analysis.
Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation
Recurrent neural networks (RNNs) are then trained to describe the contexts of a detected disease, based on the deep CNN features.
Unsupervised Category Discovery via Looped Deep Pseudo-Task Optimization Using a Large Scale Radiology Image Database
Obtaining semantic labels on a large scale radiology image database (215, 786 key images from 61, 845 unique patients) is a prerequisite yet bottleneck to train highly effective deep convolutional neural network (CNN) models for image recognition.
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Another effective method is transfer learning, i. e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks.
Deep convolutional networks for automated detection of posterior-element fractures on spine CT
In this work, we apply deep convolutional networks (ConvNets) for the automated detection of posterior element fractures of the spine.
DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation
We propose and evaluate several variations of deep ConvNets in the context of hierarchical, coarse-to-fine classification on image patches and regions, i. e. superpixels.
Interleaved Text/Image Deep Mining on a Very Large-Scale Radiology Database
We present an interleaved text/image deep learning system to extract and mine the semantic interactions of radiology images and reports from a national research hospital's picture archiving and communication system.
A Bottom-up Approach for Pancreas Segmentation using Cascaded Superpixels and (Deep) Image Patch Labeling
We present a bottom-up approach for pancreas segmentation in abdominal CT scans that is based on a hierarchy of information propagation by classifying image patches at different resolutions; and cascading superpixels.
Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation
By leveraging existing CAD systems, coordinates of regions or volumes of interest (ROI or VOI) for lesion candidates are generated in this step and function as input for a second tier, which is our focus in this study.
Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database for Automated Image Interpretation
We present an interleaved text/image deep learning system to extract and mine the semantic interactions of radiology images and reports from a national research hospital's Picture Archiving and Communication System.
Anatomy-specific classification of medical images using deep convolutional nets
We show that a data augmentation approach can help to enrich the data set and improve classification performance.
Deep convolutional networks for pancreas segmentation in CT imaging
We evaluate our method on CT images of 82 patients (60 for training, 2 for validation, and 20 for testing).
2D View Aggregation for Lymph Node Detection Using a Shallow Hierarchy of Linear Classifiers
In this paper, we propose a new algorithm representation of decomposing the LN detection problem into a set of 2D object detection subtasks on sampled CT slices, largely alleviating the curse of dimensionality issue.
A Bottom-Up Approach for Automatic Pancreas Segmentation in Abdominal CT Scans
Organ segmentation is a prerequisite for a computer-aided diagnosis (CAD) system to detect pathologies and perform quantitative analysis.
Detection of Sclerotic Spine Metastases via Random Aggregation of Deep Convolutional Neural Network Classifications
In testing, the CNN is employed to assign individual probabilities for a new set of N random views that are averaged at each ROI to compute a final per-candidate classification probability.
Coarse-to-Fine Classification via Parametric and Nonparametric Models for Computer-Aided Diagnosis
Classification is one of the core problems in Computer-Aided Diagnosis (CAD), targeting for early cancer detection using 3D medical imaging interpretation.
Semantic Context Forests for Learning-Based Knee Cartilage Segmentation in 3D MR Images
The automatic segmentation of human knee cartilage from 3D MR images is a useful yet challenging task due to the thin sheet structure of the cartilage with diffuse boundaries and inhomogeneous intensities.
