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Found 55 papers, 24 papers with code
MAISI: Medical AI for Synthetic Imaging
Medical imaging analysis faces challenges such as data scarcity, high annotation costs, and privacy concerns.
Robust Real-time Segmentation of Bio-Morphological Features in Human Cherenkov Imaging during Radiotherapy via Deep Learning
To address the challenge of limited annotation of these features in Cherenkov images, a transfer learning strategy was applied.
A Short Review and Evaluation of SAM2's Performance in 3D CT Image Segmentation
Since the release of Segment Anything 2 (SAM2), the medical imaging community has been actively evaluating its performance for 3D medical image segmentation.
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.
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.
VISTA3D: Versatile Imaging SegmenTation and Annotation model for 3D Computed Tomography
The novel model design and training recipe represent a promising step toward developing a versatile medical image foundation model and will serve as a valuable foundation for CT image analysis.
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).
Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomography
However, these AI models often struggle with flexibility for partially annotated datasets and extensibility for new classes due to limitations in the one-hot encoding, architectural design, and learning scheme.
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.
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.
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.
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").
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.
Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training
We curate a large-scale dataset to enable pre-training of 3D medical radiology images (MRI and CT).
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.
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.
AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weeks
Annotating medical images, particularly for organ segmentation, is laborious and time-consuming.
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.
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
The proposed model is developed from an assembly of 14 datasets, using a total of 3, 410 CT scans for training and then evaluated on 6, 162 external CT scans from 3 additional datasets.
Ranked #1 on
Organ Segmentation
on BTCV
MONAI: An open-source framework for deep learning in healthcare
For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e. g. geometry, physiology, physics) of medical data being processed.
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.
Longitudinal Variability Analysis on Low-dose Abdominal CT with Deep Learning-based Segmentation
Metabolic health is increasingly implicated as a risk factor across conditions from cardiology to neurology, and efficiency assessment of body composition is critical to quantitatively characterizing these relationships.
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.
Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomography
In cross-validation on screening chest CTs from the NLST, our methods (0. 785 and 0. 786 AUC respectively) significantly outperform a cross-sectional approach (0. 734 AUC) and match the discriminative performance of the leading longitudinal medical imaging algorithm (0. 779 AUC) on benign versus malignant classification.
A Comparative Study of Confidence Calibration in Deep Learning: From Computer Vision to Medical Imaging
We believe that this study has merits to guide readers to choose calibration models and understand gaps between general computer vision and medical imaging domains.
Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
Transformer, the latest technological advance of deep learning, has gained prevalence in natural language processing or computer vision.
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.
MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images
MONAI Label allows researchers to make incremental improvements to their AI-based annotation application by making them available to other researchers and clinicians alike.
Characterizing Renal Structures with 3D Block Aggregate Transformers
Efficiently quantifying renal structures can provide distinct spatial context and facilitate biomarker discovery for kidney morphology.
Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images
Semantic segmentation of brain tumors is a fundamental medical image analysis task involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient and successively studying the progression of the malignant entity.
Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis
Vision Transformers (ViT)s have shown great performance in self-supervised learning of global and local representations that can be transferred to downstream applications.
Ranked #1 on
Medical Image Segmentation
on Synapse multi-organ CT
(using extra training data)
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.
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.
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.
UNETR: Transformers for 3D Medical Image Segmentation
Inspired by the recent success of transformers for Natural Language Processing (NLP) in long-range sequence learning, we reformulate the task of volumetric (3D) medical image segmentation as a sequence-to-sequence prediction problem.
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.
Development and Characterization of a Chest CT Atlas
For the entire study cohort, the optimized pipeline achieves a registration success rate of 91. 7%.
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.
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.
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).
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.
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.
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).
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.
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.
More Knowledge is Better: Cross-Modality Volume Completion and 3D+2D Segmentation for Intracardiac Echocardiography Contouring
Using catheter ablation to treat atrial fibrillation increasingly relies on intracardiac echocardiography (ICE) for an anatomical delineation of the left atrium and the pulmonary veins that enter the atrium.
