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Found 150 papers, 58 papers with code
KPIs 2024 Challenge: Advancing Glomerular Segmentation from Patch- to Slide-Level
Chronic kidney disease (CKD) is a major global health issue, affecting over 10% of the population and causing significant mortality.
CASC-AI: Consensus-aware Self-corrective AI Agents for Noise Cell Segmentation
The Consensus Matrix defines regions where both the AI and annotators agree on cell and non-cell annotations, which are prioritized with stronger supervision.
Expanding Training Data for Endoscopic Phenotyping of Eosinophilic Esophagitis
Eosinophilic esophagitis (EoE) is a chronic esophageal disorder marked by eosinophil-dominated inflammation.
Enhanced Feature-based Image Stitching for Endoscopic Videos in Pediatric Eosinophilic Esophagitis
Image stitching techniques address this issue by providing a continuous and complete visualization of the examined area.
Sparse Measurement Medical CT Reconstruction using Multi-Fused Block Matching Denoising Priors
Instead of directly using a 3D prior, this work proposes a BM3D Multi Slice Fusion (BM3D-MSF) prior that uses multiple 2D image denoisers fused to act as a fully 3D prior model in Plug and Play reconstruction approach.
Post-Training Quantization for 3D Medical Image Segmentation: A Practical Study on Real Inference Engines
Second, we convert this fake quantization into real quantization via TensorRT engine on real GPUs, resulting in real-world reductions in model size and inference latency.
Polyhedra Encoding Transformers: Enhancing Diffusion MRI Analysis Beyond Voxel and Volumetric Embedding
Diffusion-weighted Magnetic Resonance Imaging (dMRI) is an essential tool in neuroimaging.
Fast-RF-Shimming: Accelerate RF Shimming in 7T MRI using Deep Learning
This approach offers a faster and more efficient solution to RF shimming challenges in UHF MRI.
PySpatial: A High-Speed Whole Slide Image Pathomics Toolkit
These results highlight PySpatial's potential to handle large-scale WSI analysis with enhanced efficiency and accuracy, paving the way for broader applications in digital pathology.
Scale-up Unlearnable Examples Learning with High-Performance Computing
A prominent method within this area, called Unlearnable Clustering (UC), has shown improved UE performance with larger batch sizes but was previously limited by computational resources.
Efficient MedSAMs: Segment Anything in Medical Images on Laptop
Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice.
ASIGN: An Anatomy-aware Spatial Imputation Graphic Network for 3D Spatial Transcriptomics
Expanding ST to three-dimensional (3D) volumes is challenging due to the prohibitive costs; a 2D ST acquisition already costs over 50 times more than whole slide imaging (WSI), and a full 3D volume with 10 sections can be an order of magnitude more expensive.
GloFinder: AI-empowered QuPath Plugin for WSI-level Glomerular Detection, Visualization, and Curation
Artificial intelligence (AI) has demonstrated significant success in automating the detection of glomeruli, the key functional units of the kidney, from whole slide images (WSIs) in kidney pathology.
Glo-In-One-v2: Holistic Identification of Glomerular Cells, Tissues, and Lesions in Human and Mouse Histopathology
In this study, we leverage the Glo-In-One toolkit to version 2 with fine-grained segmentation capabilities, curating 14 distinct labels for tissue regions, cells, and lesions across a dataset of 23, 529 annotated glomeruli across human and mouse histopathology data.
Cross-organ Deployment of EOS Detection AI without Retraining: Feasibility and Limitation
Interestingly, eosinophils are predominantly located in the gastrointestinal (GI) tract, which has prompted the release of numerous deep learning models trained on GI data.
How Good Are We? Evaluating Cell AI Foundation Models in Kidney Pathology with Human-in-the-Loop Enrichment
This study establishes a benchmark for the development and deployment of cell vision foundation models tailored for real-world data applications.
Brain age identification from diffusion MRI synergistically predicts neurodegenerative disease
We observe difference between our dMRI-based brain age and T1w MRI-based brain age across stages of neurodegeneration, with dMRI-based brain age being older than T1w MRI-based brain age in participants transitioning from cognitively normal (CN) to mild cognitive impairment (MCI), but younger in participants already diagnosed with Alzheimer's disease (AD).
Automatic Image Unfolding and Stitching Framework for Esophageal Lining Video Based on Density-Weighted Feature Matching
Endoscopy is a crucial tool for diagnosing the gastrointestinal tract, but its effectiveness is often limited by a narrow field of view and the dynamic nature of the internal environment, especially in the esophagus, where complex and repetitive patterns make image stitching challenging.
Multi-Modality Conditioned Variational U-Net for Field-of-View Extension in Brain Diffusion MRI
We hypothesize that by this design the proposed framework can enhance the imputation performance of the dMRI scans and therefore be useful for repairing whole-brain tractography in corrupted dMRI scans with incomplete FOV.
Influence of Early through Late Fusion on Pancreas Segmentation from Imperfectly Registered Multimodal MRI
We trained a collection of basic UNets with different fusion points, spanning from early to late, to assess how early through late fusion influenced segmentation performance on imperfectly aligned images.
Reconstructing physiological signals from fMRI across the adult lifespan
Interactions between the brain and body are of fundamental importance for human behavior and health.
Optimizing Transmit Field Inhomogeneity of Parallel RF Transmit Design in 7T MRI using Deep Learning
We propose a two-step deep learning strategy.
Cross-Species Data Integration for Enhanced Layer Segmentation in Kidney Pathology
Cross-species homologous data, such as mouse kidney data, which exhibits high structural and feature similarity to human kidneys, has the potential to enhance model performance on human datasets.
Persistence Image from 3D Medical Image: Superpixel and Optimized Gaussian Coefficient
Topological data analysis (TDA) uncovers crucial properties of objects in medical imaging.
Assessment of Cell Nuclei AI Foundation Models in Kidney Pathology
Among the evaluated models, CellViT demonstrated superior performance in segmenting nuclei in kidney pathology.
AI Foundation Models in Remote Sensing: A Survey
Artificial Intelligence (AI) technologies have profoundly transformed the field of remote sensing, revolutionizing data collection, processing, and analysis.
GLAM: Glomeruli Segmentation for Human Pathological Lesions using Adapted Mouse Model
To answer this question, we introduced GLAM, a deep learning study for fine-grained segmentation of human kidney lesions using a mouse model, addressing mouse-to-human transfer learning, by evaluating different learning strategies for segmenting human pathological lesions using zero-shot transfer learning and hybrid learning by leveraging mouse samples.
Dataset Distillation in Medical Imaging: A Feasibility Study
Our extensive experiments across multiple medical datasets reveal that data distillation can significantly reduce dataset size while maintaining comparable model performance to that achieved with the full dataset, suggesting that a small, representative sample of images can serve as a reliable indicator of distillation success.
PFPs: Prompt-guided Flexible Pathological Segmentation for Diverse Potential Outcomes Using Large Vision and Language Models
The Vision Foundation Model has recently gained attention in medical image analysis.
HoloHisto: End-to-end Gigapixel WSI Segmentation with 4K Resolution Sequential Tokenization
In this paper, we propose the holistic histopathology (HoloHisto) segmentation method to achieve end-to-end segmentation on gigapixel WSIs, whose maximum resolution is above 80, 000$\times$70, 000 pixels.
HATs: Hierarchical Adaptive Taxonomy Segmentation for Panoramic Pathology Image Analysis
Panoramic image segmentation in computational pathology presents a remarkable challenge due to the morphologically complex and variably scaled anatomy.
Weighted Circle Fusion: Ensembling Circle Representation from Different Object Detection Results
Additionally, we assess the efficiency of two annotation methods, fully manual annotation and a human-in-the-loop (HITL) approach, in labeling 200, 000 glomeruli.
Enhancing Single-Slice Segmentation with 3D-to-2D Unpaired Scan Distillation
2D single-slice abdominal computed tomography (CT) enables the assessment of body habitus and organ health with low radiation exposure.
mTREE: Multi-Level Text-Guided Representation End-to-End Learning for Whole Slide Image Analysis
Multi-modal learning adeptly integrates visual and textual data, but its application to histopathology image and text analysis remains challenging, particularly with large, high-resolution images like gigapixel Whole Slide Images (WSIs).
ExtremeMETA: High-speed Lightweight Image Segmentation Model by Remodeling Multi-channel Metamaterial Imagers
In this paper, we propose a large kernel lightweight segmentation model, ExtremeMETA.
Field-of-View Extension for Brain Diffusion MRI via Deep Generative Models
We hypothesize that the imputed image with complete FOV can improve the whole-brain tractography for corrupted data with incomplete FOV.
Adaptive Patching for High-resolution Image Segmentation with Transformers
For high-resolution images, e. g. microscopic pathology images, the quadratic compute and memory cost prohibits the use of an attention-based model, if we are to use smaller patch sizes that are favorable in segmentation.
Circle Representation for Medical Instance Object Segmentation
Recently, circle representation has been introduced for medical imaging, designed specifically to enhance the detection of instance objects that are spherically shaped (e. g., cells, glomeruli, and nuclei).
PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation
Understanding the anatomy of renal pathology is crucial for advancing disease diagnostics, treatment evaluation, and clinical research.
Foundation Models for Biomedical Image Segmentation: A Survey
This transformative technology, originally developed for general-purpose computer vision, has found rapid application in medical image processing.
$M^{2}$Fusion: Bayesian-based Multimodal Multi-level Fusion on Colorectal Cancer Microsatellite Instability Prediction
The contribution of the paper is three-fold: (1) $M^{2}$Fusion is the first pipeline of multi-level fusion on pathology WSI and 3D radiology CT image for MSI prediction; (2) CT images are the first time integrated into multimodal fusion for CRC MSI prediction; (3) feature-level fusion strategy is evaluated on both Transformer-based and CNN-based method.
Nucleus subtype classification using inter-modality learning
In this paper, we propose to use inter-modality learning to label previously un-labelable cell types on virtual H&E.
Evaluation of Mean Shift, ComBat, and CycleGAN for Harmonizing Brain Connectivity Matrices Across Sites
First, we plotted the graph measures and used coefficient of variation (CoV) and the Mann-Whitney U test to evaluate different methods' effectiveness in removing site effects on the matrices and the derived graph measures.
Super-resolution multi-contrast unbiased eye atlases with deep probabilistic refinement
These variations limit the feasibility and robustness of generalizing population-wise features of eye organs to an unbiased spatial reference.
Predicting Age from White Matter Diffusivity with Residual Learning
We find that the ResNet model captures subtler, non-macrostructural features for brain age prediction.
DeformUX-Net: Exploring a 3D Foundation Backbone for Medical Image Segmentation with Depthwise Deformable Convolution
We hypothesize that deformable convolution can be an exploratory alternative to combine all advantages from the previous operators, providing long-range dependency, adaptive spatial aggregation and computational efficiency as a foundation backbone.
Deep conditional generative models for longitudinal single-slice abdominal computed tomography harmonization
We further evaluate our method's capability to harmonize longitudinal positional variation on 1033 subjects from the Baltimore Longitudinal Study of Aging (BLSA) dataset, which contains longitudinal single abdominal slices, and confirmed that our method can harmonize the slice positional variance in terms of visceral fat area.
Enhancing Hierarchical Transformers for Whole Brain Segmentation with Intracranial Measurements Integration
Subsequently, the model is finetuned with 45 T1w 3D volumes from Open Access Series Imaging Studies (OASIS) where both 133 whole brain classes and TICV/PFV labels are available.
Evaluation Kidney Layer Segmentation on Whole Slide Imaging using Convolutional Neural Networks and Transformers
However, very few, if any, deep learning based approaches have been applied to kidney layer structure segmentation.
Cell Spatial Analysis in Crohn's Disease: Unveiling Local Cell Arrangement Pattern with Graph-based Signatures
This analysis is based on data collected at the two research institutes.
Eosinophils Instance Object Segmentation on Whole Slide Imaging Using Multi-label Circle Representation
In this paper, we propose the multi-label CircleSnake model for instance segmentation on Eos.
Deep Learning-Based Open Source Toolkit for Eosinophil Detection in Pediatric Eosinophilic Esophagitis
Eosinophilic Esophagitis (EoE) is a chronic, immune/antigen-mediated esophageal disease, characterized by symptoms related to esophageal dysfunction and histological evidence of eosinophil-dominant inflammation.
Spatial Pathomics Toolkit for Quantitative Analysis of Podocyte Nuclei with Histology and Spatial Transcriptomics Data in Renal Pathology
Podocytes, specialized epithelial cells that envelop the glomerular capillaries, play a pivotal role in maintaining renal health.
Leverage Weakly Annotation to Pixel-wise Annotation via Zero-shot Segment Anything Model for Molecular-empowered Learning
In this paper, we explore the potential of bypassing pixel-level delineation by employing the recent segment anything model (SAM) on weak box annotation in a zero-shot learning approach.
Multi-scale Multi-site Renal Microvascular Structures Segmentation for Whole Slide Imaging in Renal Pathology
While deep learning-based methods offer a solution for automatic segmentation, most suffer from a limitation: they are designed for and restricted to training on single-site, single-scale data.
High-performance Data Management for Whole Slide Image Analysis in Digital Pathology
The performance evaluation encompasses two key scenarios: (1) a pure CPU-based image analysis scenario ("CPU scenario"), and (2) a GPU-based deep learning framework scenario ("GPU scenario").
Digital Modeling on Large Kernel Metamaterial Neural Network
However, the digital design of the metamaterial neural network (MNN) is fundamentally limited by its physical limitations, such as precision, noise, and bandwidth during fabrication.
Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images
Many anomaly detection approaches, especially deep learning methods, have been recently developed to identify abnormal image morphology by only employing normal images during training.
All-in-SAM: from Weak Annotation to Pixel-wise Nuclei Segmentation with Prompt-based Finetuning
The Segment Anything Model (SAM) is a recently proposed prompt-based segmentation model in a generic zero-shot segmentation approach.
Intelligent Multi-channel Meta-imagers for Accelerating Machine Vision
Rapid developments in machine vision have led to advances in a variety of industries, from medical image analysis to autonomous systems.
Robust Fiber Orientation Distribution Function Estimation Using Deep Constrained Spherical Deconvolution for Diffusion MRI
From the experimental results, the proposed data-driven framework outperforms the existing benchmarks in repeated fODF estimation.
Multi-Contrast Computed Tomography Atlas of Healthy Pancreas
Briefly, DEEDs affine and non-rigid registration are performed to transfer patient abdominal volumes to a fixed high-resolution atlas template.
Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning
The contribution of this paper is threefold: (1) We proposed a molecular-empowered learning scheme for multi-class cell segmentation using partial labels from lay annotators; (2) The proposed method integrated Giga-pixel level molecular-morphology cross-modality registration, molecular-informed annotation, and molecular-oriented segmentation model, so as to achieve significantly superior performance via 3 lay annotators as compared with 2 experienced pathologists; (3) A deep corrective learning (learning with imperfect label) method is proposed to further improve the segmentation performance using partially annotated noisy data.
An Accelerated Pipeline for Multi-label Renal Pathology Image Segmentation at the Whole Slide Image Level
Our proposed method's innovative contribution is two-fold: (1) a Docker is released for an end-to-end slide-wise multi-tissue segmentation for WSIs; and (2) the pipeline is deployed on a GPU to accelerate the prediction, achieving better segmentation quality in less time.
An End-to-end Pipeline for 3D Slide-wise Multi-stain Renal Pathology Registration
In this paper, we provide a Docker for an end-to-end 3D slide-wise registration pipeline on needle biopsy serial sections in a multi-stain paradigm.
Exploring shared memory architectures for end-to-end gigapixel deep learning
The M1 Ultra SoC was able to train the model directly on gigapixel images (16000$\times$64000 pixels, 1. 024 billion pixels) with a batch size of 1 using over 100 GB of unified memory for the process at an average speed of 1 minute and 21 seconds per batch with Tensorflow 2/Keras.
Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging
However, it does not consistently achieve satisfying performance for dense instance object segmentation, even with 20 prompts (clicks/boxes) on each image.
Zero-shot CT Field-of-view Completion with Unconditional Generative Diffusion Prior
Anatomically consistent field-of-view (FOV) completion to recover truncated body sections has important applications in quantitative analyses of computed tomography (CT) with limited FOV.
Cross-scale Multi-instance Learning for Pathological Image Diagnosis
Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology.
A Unified Learning Model for Estimating Fiber Orientation Distribution Functions on Heterogeneous Multi-shell Diffusion-weighted MRI
Diffusion-weighted (DW) MRI measures the direction and scale of the local diffusion process in every voxel through its spectrum in q-space, typically acquired in one or more shells.
Scaling Up 3D Kernels with Bayesian Frequency Re-parameterization for Medical Image Segmentation
We hypothesize that convolution with LK sizes is limited to maintain an optimal convergence for locality learning.
Single Slice Thigh CT Muscle Group Segmentation with Domain Adaptation and Self-Training
The proposed pipeline is effective and robust in extracting muscle groups on 2D single slice CT thigh images. The container is available for public use at https://github. com/MASILab/DA_CT_muscle_seg
CircleSnake: Instance Segmentation with Circle Representation
Circle representation has recently been introduced as a medical imaging optimized representation for more effective instance object detection on ball-shaped medical objects.
Adaptive Contrastive Learning with Dynamic Correlation for Multi-Phase Organ Segmentation
We evaluate our proposed approach on multi-organ segmentation with both non-contrast CT (NCCT) datasets and the MICCAI 2015 BTCV Challenge contrast-enhance CT (CECT) datasets.
3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation
Hierarchical transformers (e. g., Swin Transformers) reintroduced several ConvNet priors and further enhanced the practical viability of adapting volumetric segmentation in 3D medical datasets.
UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation
Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical image analysis.
Reducing Positional Variance in Cross-sectional Abdominal CT Slices with Deep Conditional Generative Models
External experiments on 20 subjects from the Baltimore Longitudinal Study of Aging (BLSA) dataset that contains longitudinal single abdominal slices validate that our method can harmonize the slice positional variance in terms of muscle and visceral fat area.
Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised Learning
In order to extract and separate compound figures into usable individual images for downstream learning, we propose a simple compound figure separation (SimCFS) framework without using the traditionally required detection bounding box annotations, with a new loss function and a hard case simulation.
Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images
Multi-instance learning (MIL) is widely used in the computer-aided interpretation of pathological Whole Slide Images (WSIs) to solve the lack of pixel-wise or patch-wise annotations.
Body Composition Assessment with Limited Field-of-view Computed Tomography: A Semantic Image Extension Perspective
We evaluate the validity of the proposed method in automatic BC assessment using lung screening CT with limited FOV.
Omni-Seg: A Scale-aware Dynamic Network for Renal Pathological Image Segmentation
The contribution of this paper is three-fold: (1) a novel scale-aware controller is proposed to generalize the dynamic neural network from single-scale to multi-scale; (2) semi-supervised consistency regularization of pseudo-labels is introduced to model the inter-scale correlation of unannotated tissue types into a single end-to-end learning paradigm; and (3) superior scale-aware generalization is evidenced by directly applying a model trained on human kidney images to mouse kidney images, without retraining.
Glo-In-One: Holistic Glomerular Detection, Segmentation, and Lesion Characterization with Large-scale Web Image Mining
To leverage the performance of the Glo-In-One toolkit, we introduce self-supervised deep learning to glomerular quantification via large-scale web image mining.
Pseudo-Label Guided Multi-Contrast Generalization for Non-Contrast Organ-Aware Segmentation
In this paper, we propose a novel unsupervised approach that leverages pairwise contrast-enhanced CT (CECT) context to compute non-contrast segmentation without ground-truth label.
Deep Multi-modal Fusion of Image and Non-image Data in Disease Diagnosis and Prognosis: A Review
The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during routine practice.
Survival Prediction of Brain Cancer with Incomplete Radiology, Pathology, Genomics, and Demographic Data
Thus, there are still open questions on how to effectively predict brain cancer survival from the incomplete radiological, pathological, genomic, and demographic data (e. g., one or more modalities might not be collected for a patient).
Characterizing Renal Structures with 3D Block Aggregate Transformers
Efficiently quantifying renal structures can provide distinct spatial context and facilitate biomarker discovery for kidney morphology.
Holistic Fine-grained GGS Characterization: From Detection to Unbalanced Classification
Recent studies have demonstrated the diagnostic and prognostic values of global glomerulosclerosis (GGS) in IgA nephropathy, aging, and end-stage renal disease.
MAg: a simple learning-based patient-level aggregation method for detecting microsatellite instability from whole-slide images
Such algorithms can be summarized as (1) patch-level MSI/MSS prediction, and (2) patient-level aggregation.
Omni-Seg: A Single Dynamic Network for Multi-label Renal Pathology Image Segmentation using Partially Labeled Data
Computer-assisted quantitative analysis on Giga-pixel pathology images has provided a new avenue in histology examination.
Semi-Supervised Contrastive Learning for Remote Sensing: Identifying Ancient Urbanization in the South Central Andes
Recently, systematic manual survey of satellite and aerial imagery has enabled continuous distributional views of archaeological phenomena at interregional scales.
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed.
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
Random Multi-Channel Image Synthesis for Multiplexed Immunofluorescence Imaging
Our contribution is three-fold: (1) a single deep network framework is proposed to tackle missing stain in MxIF; (2) the proposed 'N-to-N' strategy reduces theoretical four years of computational time to 20 hours when covering all possible missing stains scenarios, with up to five missing stains (e. g., '(N-1)-to-1', '(N-2)-to-2'); and (3) this work is the first comprehensive experimental study of investigating cross-stain synthesis in MxIF.
Evaluating Transformer-based Semantic Segmentation Networks for Pathological Image Segmentation
For a more comprehensive analysis, we also compare the transformer-based models with six major traditional CNN-based models.
Technical Report: Quality Assessment Tool for Machine Learning with Clinical CT
Image Quality Assessment (IQA) is important for scientific inquiry, especially in medical imaging and machine learning.
Lung Cancer Risk Estimation with Incomplete Data: A Joint Missing Imputation Perspective
To our knowledge, it is the first generative adversarial model that addresses multi-modal missing imputation by modeling the joint distribution of image and non-image data.
Compound Figure Separation of Biomedical Images with Side Loss
Our technical contribution is three-fold: (1) we introduce a new side loss that is designed for compound figure separation; (2) we introduce an intra-class image augmentation method to simulate hard cases; (3) the proposed framework enables an efficient deployment to new classes of images, without requiring resource extensive bounding box annotations.
VoxelEmbed: 3D Instance Segmentation and Tracking with Voxel Embedding based Deep Learning
Recently, pixel embedding-based cell instance segmentation and tracking provided a neat and generalizable computing paradigm for understanding cellular dynamics.
Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation
Medical image segmentation, or computing voxelwise semantic masks, is a fundamental yet challenging task to compute a voxel-level semantic mask.
SimTriplet: Simple Triplet Representation Learning with a Single GPU
The contribution of the paper is three-fold: (1) The proposed SimTriplet method takes advantage of the multi-view nature of medical images beyond self-augmentation; (2) The method maximizes both intra-sample and inter-sample similarities via triplets from positive pairs, without using negative samples; and (3) The recent mix precision training is employed to advance the training by only using a single GPU with 16GB memory.
Contrastive Learning Meets Transfer Learning: A Case Study In Medical Image Analysis
In this paper, we investigate the feasibility of aligning BiT with SimSiam.
BEDS: Bagging ensemble deep segmentation for nucleus segmentation with testing stage stain augmentation
Reducing outcome variance is an essential task in deep learning based medical image analysis.
Improve Global Glomerulosclerosis Classification with Imbalanced Data using CircleMix Augmentation
Different from the recently proposed CutMix method, the CircleMix augmentation is optimized for the ball-shaped biomedical objects, such as glomeruli.
WearMask: Fast In-browser Face Mask Detection with Serverless Edge Computing for COVID-19
The serverless edge-computing design minimizes the extra hardware costs (e. g., specific devices or cloud computing servers).
ASIST: Annotation-free Synthetic Instance Segmentation and Tracking by Adversarial Simulations
Contribution: The contribution of this paper is three-fold: (1) the proposed method aggregates adversarial simulations and single-stage pixel-embedding based deep learning; (2) the method is assessed with both the cellular (i. e., HeLa cells) and subcellular (i. e., microvilli) objects; and (3) to the best of our knowledge, this is the first study to explore annotation-free instance segmentation and tracking study for microscope videos.
RAP-Net: Coarse-to-Fine Multi-Organ Segmentation with Single Random Anatomical Prior
We combine the anatomical prior with corresponding extracted patches to preserve the anatomical locations and boundary information for performing high-resolution segmentation across all organs in a single model.
Multi-Contrast Computed Tomography Healthy Kidney Atlas
However, there is no abdominal and retroperitoneal organs atlas framework for multi-contrast CT.
Limitation of Acyclic Oriented Graphs Matching as Cell Tracking Accuracy Measure when Evaluating Mitosis
Multi-object tracking (MOT) in computer vision and cell tracking in biomedical image analysis are two similar research fields, whose common aim is to achieve instance level object detection/segmentation and associate such objects across different video frames.
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.
Development and Characterization of a Chest CT Atlas
For the entire study cohort, the optimized pipeline achieves a registration success rate of 91. 7%.
ASIST: Annotation-free synthetic instance segmentation and tracking for microscope video analysis
Instance object segmentation and tracking provide comprehensive quantification of objects across microscope videos.
CaCL: Class-aware Codebook Learning for Weakly Supervised Segmentation on Diffuse Image Patterns
Our contribution is three-fold: (1) we approach the weakly supervised segmentation from a novel codebook learning perspective; (2) the CaCL algorithm segments diffuse image patterns rather than focal objects; and (3) the proposed algorithm is implemented in a multi-task framework based on Vector Quantised-Variational AutoEncoder (VQ-VAE) via joint image reconstruction, classification, feature embedding, and segmentation.
GAN based Unsupervised Segmentation: Should We Match the Exact Number of Objects
To match the number of objects at the micro-level, the novel fluorescence-based micro-level matching approach was presented.
Deep Multi-path Network Integrating Incomplete Biomarker and Chest CT Data for Evaluating Lung Cancer Risk
The network can be trained end-to-end with both medical image features and CDEs/biomarkers, or make a prediction with single modality.
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.
EasierPath: An Open-source Tool for Human-in-the-loop Deep Learning of Renal Pathology
Inspired by the recent "human-in-the-loop" strategy, we developed EasierPath, an open-source tool to integrate human physicians and deep learning algorithms for efficient large-scale pathological image quantification as a loop.
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.
Instance Segmentation for Whole Slide Imaging: End-to-End or Detect-Then-Segment
In this paper, we assess if the end-to-end instance segmentation framework is optimal for high-resolution WSI objects by comparing Mask-RCNN with our proposed detect-then-segment framework.
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).
Map3D: Registration Based Multi-Object Tracking on 3D Serial Whole Slide Images
However, the 3D identification and association of large-scale glomeruli on renal pathology is challenging due to large tissue deformation, missing tissues, and artifacts from WSI.
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.
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.
Semi-supervised Contrastive Learning Using Partial Label Information
In some learning problems, partial label information can be inferred from otherwise unlabeled examples and used to further improve the model.
Outlier Guided Optimization of Abdominal Segmentation
We built on a pre-trained 3D U-Net model for abdominal multi-organ segmentation and augmented the dataset either with outlier data (e. g., exemplars for which the baseline algorithm failed) or inliers (e. g., exemplars for which the baseline algorithm worked).
Validation and Optimization of Multi-Organ Segmentation on Clinical Imaging Archives
A 2015 MICCAI challenge spurred substantial innovation in multi-organ abdominal CT segmentation with both traditional and deep learning methods.
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.
Internal-transfer Weighting of Multi-task Learning for Lung Cancer Detection
To improve performance on the primary task, we propose an Internal-Transfer Weighting (ITW) strategy to suppress the loss functions on auxiliary tasks for the final stages of training.
Stochastic tissue window normalization of deep learning on computed tomography
We evaluate the effectiveness of both with and without using soft tissue window normalization on multisite CT cohorts.
Contrast Phase Classification with a Generative Adversarial Network
Dynamic contrast enhanced computed tomography (CT) is an imaging technique that provides critical information on the relationship of vascular structure and dynamics in the context of underlying anatomy.
Deep Multi-task Prediction of Lung Cancer and Cancer-free Progression from Censored Heterogenous Clinical Imaging
Annual low dose computed tomography (CT) lung screening is currently advised for individuals at high risk of lung cancer (e. g., heavy smokers between 55 and 80 years old).
Semi-Supervised Multi-Organ Segmentation through Quality Assurance Supervision
The contributions of the proposed method are threefold: We show that (1) the QA scores can be used as a loss function to perform semi-supervised learning for unlabeled data, (2) the well trained discriminator is learnt by QA score rather than traditional true/false, and (3) the performance of multi-organ segmentation on unlabeled datasets can be fine-tuned with more robust and higher accuracy than the original baseline method.
Distanced LSTM: Time-Distanced Gates in Long Short-Term Memory Models for Lung Cancer Detection
To model both regular and irregular longitudinal samples, we generalize the LSTM model with the Distanced LSTM (DLSTM) for temporally varied acquisitions.
Generalizing Deep Whole Brain Segmentation for Pediatric and Post-Contrast MRI with Augmented Transfer Learning
Generalizability is an important problem in deep neural networks, especially in the context of the variability of data acquisition in clinical magnetic resonance imaging (MRI).
Enabling Multi-Shell b-Value Generalizability of Data-Driven Diffusion Models with Deep SHORE
In the in-vivo human data, Deep SHORE was more consistent across scanners with 0. 63 relative to other multi-shell methods 0. 39, 0. 52 and 0. 57 in terms of ACC.
Fully Automatic Liver Attenuation Estimation Combing CNN Segmentation and Morphological Operations
Manually tracing regions of interest (ROIs) within the liver is the de facto standard method for measuring liver attenuation on computed tomography (CT) in diagnosing nonalcoholic fatty liver disease (NAFLD).
3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles
To address the first challenge, multiple spatially distributed networks were used in the SLANT method, in which each network learned contextual information for a fixed spatial location.
Lung Cancer Detection using Co-learning from Chest CT Images and Clinical Demographics
The results show that the AUC obtained from clinical demographics alone was 0. 635 while the attention network alone reached an accuracy of 0. 687.
Reproducibility Evaluation of SLANT Whole Brain Segmentation Across Clinical Magnetic Resonance Imaging Protocols
Recently, we obtained a clinically acquired, multi-sequence MRI brain cohort with 1480 clinically acquired, de-identified brain MRI scans on 395 patients using seven different MRI protocols.
Montage based 3D Medical Image Retrieval from Traumatic Brain Injury Cohort using Deep Convolutional Neural Network
For instance, in a typical sample of clinical TBI imaging cohort, only ~15% of CT scans actually contain whole brain CT images suitable for volumetric brain analyses; the remaining are partial brain or non-brain images.
Towards Machine Learning Prediction of Deep Brain Stimulation (DBS) Intra-operative Efficacy Maps
We show an improvement in the classification of intraoperative stimulation coordinates as a positive response in reduction of symptoms with AUC of 0. 670 compared to a baseline registration-based approach, which achieves an AUC of 0. 627 (p < 0. 01).
Coronary Calcium Detection using 3D Attention Identical Dual Deep Network Based on Weakly Supervised Learning
From the results, the proposed AID-Net achieved the superior performance on classification accuracy (0. 9272) and AUC (0. 9627).
Splenomegaly Segmentation on Multi-modal MRI using Deep Convolutional Networks
A clinically acquired cohort containing both T1-weighted (T1w) and T2-weighted (T2w) MRI splenomegaly scans was used to train and evaluate the performance of multi-atlas segmentation (MAS), 2D DCNN networks, and a 3D DCNN network.
SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth
SynSeg-Net is trained by using (1) unpaired intensity images from source and target modalities, and (2) manual labels only from source modality.
Data-driven Probabilistic Atlases Capture Whole-brain Individual Variation
By indexing the dictionary, the whole brain probabilistic atlases adapt to each new subject quickly and can be used as spatial priors for visualization and processing.
Spatially Localized Atlas Network Tiles Enables 3D Whole Brain Segmentation from Limited Data
Whole brain segmentation on a structural magnetic resonance imaging (MRI) is essential in non-invasive investigation for neuroanatomy.
Less is More: Simultaneous View Classification and Landmark Detection for Abdominal Ultrasound Images
However, this is a challenging problem given not only the inherent difficulties from the ultrasound modality, e. g., low contrast and large variations, but also the heterogeneity across tasks, i. e., one classification task for all views, and then one landmark detection task for each relevant view.
Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground Truth
Herein, we proposed a novel end-to-end synthesis and segmentation network (EssNet) to achieve the unpaired MRI to CT image synthesis and CT splenomegaly segmentation simultaneously without using manual labels on CT.
Improved Stability of Whole Brain Surface Parcellation with Multi-Atlas Segmentation
Herein, the MaCRUISE surface parcellation (MaCRUISEsp) method is proposed to perform the surface parcellation upon the inner, central and outer surfaces that are reconstructed from MaCRUISE.
Splenomegaly Segmentation using Global Convolutional Kernels and Conditional Generative Adversarial Networks
However, variations in both size and shape of the spleen on MRI images may result in large false positive and false negative labeling when deploying DCNN based methods.
4D Multi-atlas Label Fusion using Longitudinal Images
(2) The proposed algorithm is a longitudinal generalization of a lead-ing joint label fusion method (JLF) that has proven adaptable to a wide variety of applications.
