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Found 24 papers, 19 papers with code
Representing Part-Whole Hierarchies in Foundation Models by Learning Localizability, Composability, and Decomposability from Anatomy via Self-Supervision
Humans effortlessly interpret images by parsing them into part-whole hierarchies; deep learning excels in learning multi-level feature spaces, but they often lack explicit coding of part-whole relations, a prominent property of medical imaging.
Representing Part-Whole Hierarchies in Foundation Models by Learning Localizability Composability and Decomposability from Anatomy via Self Supervision
Humans effortlessly interpret images by parsing them into part-whole hierarchies; deep learning excels in learning multi-level feature spaces but they often lack explicit coding of part-whole relations a prominent property of medical imaging.
Learning Anatomically Consistent Embedding for Chest Radiography
Self-supervised learning (SSL) approaches have recently shown substantial success in learning visual representations from unannotated images.
Foundation Ark: Accruing and Reusing Knowledge for Superior and Robust Performance
To realize this vision, we have developed Ark, a framework that accrues and reuses knowledge from heterogeneous expert annotations in various datasets.
Towards Foundation Models Learned from Anatomy in Medical Imaging via Self-Supervision
Human anatomy is the foundation of medical imaging and boasts one striking characteristic: its hierarchy in nature, exhibiting two intrinsic properties: (1) locality: each anatomical structure is morphologically distinct from the others; and (2) compositionality: each anatomical structure is an integrated part of a larger whole.
Large-batch Optimization for Dense Visual Predictions
To address this challenge, we propose a simple yet effective algorithm, named Adaptive Gradient Variance Modulator (AGVM), which can train dense visual predictors with very large batch size, enabling several benefits more appealing than prior arts.
Unifying Visual Perception by Dispersible Points Learning
The method, called UniHead, views different visual perception tasks as the dispersible points learning via the transformer encoder architecture.
DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis
Discriminative learning, restorative learning, and adversarial learning have proven beneficial for self-supervised learning schemes in computer vision and medical imaging.
CAiD: Context-Aware Instance Discrimination for Self-supervised Learning in Medical Imaging
Our extensive experiments demonstrate that CAiD (1) enriches representations learned from existing instance discrimination methods; (2) delivers more discriminative features by adequately capturing finer contextual information from individual medial images; and (3) improves reusability of low/mid-level features compared to standard instance discriminative methods.
Seeking an Optimal Approach for Computer-Aided Pulmonary Embolism Detection
At the image level, we compare convolutional neural networks (CNNs) with vision transformers, and contrast self-supervised learning (SSL) with supervised learning, followed by an evaluation of transfer learning compared with training from scratch.
A Systematic Benchmarking Analysis of Transfer Learning for Medical Image Analysis
Transfer learning from supervised ImageNet models has been frequently used in medical image analysis.
Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-supervised Learning
This paper introduces a new concept called "transferable visual words" (TransVW), aiming to achieve annotation efficiency for deep learning in medical image analysis.
Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration
To this end, we train deep models to learn semantically enriched visual representation by self-discovery, self-classification, and self-restoration of the anatomy underneath medical images, resulting in a semantics-enriched, general-purpose, pre-trained 3D model, named Semantic Genesis.
Ranked #1 on
Lung Nodule Detection
on LUNA2016 FPRED
Models Genesis
Transfer learning from natural images to medical images has been established as one of the most practical paradigms in deep learning for medical image analysis.
Computer Aided Detection for Pulmonary Embolism Challenge (CAD-PE)
Objective: To generate a database of annotated computed tomography pulmonary angiographies, use it to compare the sensitivity and false positive rate of current algorithms and to develop new methods that improve such metrics.
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN).
Ranked #1 on
Medical Image Segmentation
on EM
(IoU metric)
Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis
More importantly, learning a model from scratch simply in 3D may not necessarily yield performance better than transfer learning from ImageNet in 2D, but our Models Genesis consistently top any 2D approaches including fine-tuning the models pre-trained from ImageNet as well as fine-tuning the 2D versions of our Models Genesis, confirming the importance of 3D anatomical information and significance of our Models Genesis for 3D medical imaging.
Ranked #1 on
Pulmonary Embolism Detection
on PE-CAD FPRED
Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization
Qualitative and quantitative evaluations demonstrate that the proposed method outperforms the state of the art in multi-domain image-to-image translation and that it surpasses predominant weakly-supervised localization methods in both disease detection and localization.
Surrogate Supervision for Medical Image Analysis: Effective Deep Learning From Limited Quantities of Labeled Data
We investigate the effectiveness of a simple solution to the common problem of deep learning in medical image analysis with limited quantities of labeled training data.
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet
Ranked #1 on
Semantic Segmentation
on AI-TOD
Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts
The splendid success of convolutional neural networks (CNNs) in computer vision is largely attributable to the availability of massive annotated datasets, such as ImageNet and Places.
Fine-Tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally
Intense interest in applying convolutional neural networks (CNNs) in biomedical image analysis is wide spread, but its success is impeded by the lack of large annotated datasets in biomedical imaging.
Automating Carotid Intima-Media Thickness Video Interpretation with Convolutional Neural Networks
However, each CIMT examination includes several ultrasound videos, and interpreting each of these CIMT videos involves three operations: (1) select three end-diastolic ultrasound frames (EUF) in the video, (2) localize a region of interest (ROI) in each selected frame, and (3) trace the lumen-intima interface and the media-adventitia interface in each ROI to measure CIMT.
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence.
