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Found 67 papers, 44 papers with code
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain.
Single-Layer Learnable Activation for Implicit Neural Representation (SL$^{2}$A-INR)
We have identified that these problems can be greatly alleviated by introducing a paradigm shift in INRs.
Continual Domain Incremental Learning for Privacy-aware Digital Pathology
In recent years, there has been remarkable progress in the field of digital pathology, driven by the ability to model complex tissue patterns using advanced deep-learning algorithms.
Tissue Concepts: supervised foundation models in computational pathology
The proposed method is based on multi-task learning to train a joint encoder, by combining 16 different classification, segmentation, and detection tasks on a total of 912, 000 patches.
MSA$^2$Net: Multi-scale Adaptive Attention-guided Network for Medical Image Segmentation
Medical image segmentation involves identifying and separating object instances in a medical image to delineate various tissues and structures, a task complicated by the significant variations in size, shape, and density of these features.
Segmentation of Brain Metastases in MRI: A Two-Stage Deep Learning Approach with Modality Impact Study
In this study, we first investigate the impact of different imaging modalities on segmentation performance using a 3D U-Net.
Physics-Inspired Generative Models in Medical Imaging: A Review
Physics-inspired Generative Models (GMs), in particular Diffusion Models (DMs) and Poisson Flow Models (PFMs), enhance Bayesian methods and promise great utility in medical imaging.
Unsupervised Latent Stain Adaptation for Computational Pathology
In computational pathology, deep learning (DL) models for tasks such as segmentation or tissue classification are known to suffer from domain shifts due to different staining techniques.
Computation-Efficient Era: A Comprehensive Survey of State Space Models in Medical Image Analysis
State Space Models (SSMs), specifically the \textit{\textbf{Mamba}} model with selection mechanisms and hardware-aware architecture, have garnered immense interest lately in sequential modeling and visual representation learning, challenging the dominance of transformers by providing infinite context lengths and offering substantial efficiency maintaining linear complexity in the input sequence.
LHU-Net: A Light Hybrid U-Net for Cost-Efficient, High-Performance Volumetric Medical Image Segmentation
The rise of Transformer architectures has revolutionized medical image segmentation, leading to hybrid models that combine Convolutional Neural Networks (CNNs) and Transformers for enhanced accuracy.
Enhancing Efficiency in Vision Transformer Networks: Design Techniques and Insights
Intrigued by the inherent ability of the human visual system to identify salient regions in complex scenes, attention mechanisms have been seamlessly integrated into various Computer Vision (CV) tasks.
Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospects
Medical imaging analysis has witnessed remarkable advancements even surpassing human-level performance in recent years, driven by the rapid development of advanced deep-learning algorithms.
Loss Functions in the Era of Semantic Segmentation: A Survey and Outlook
Semantic image segmentation, the process of classifying each pixel in an image into a particular class, plays an important role in many visual understanding systems.
FuseNet: Self-Supervised Dual-Path Network for Medical Image Segmentation
Semantic segmentation, a crucial task in computer vision, often relies on labor-intensive and costly annotated datasets for training.
HCA-Net: Hierarchical Context Attention Network for Intervertebral Disc Semantic Labeling
In addition, we introduce a skeletal loss term to reinforce the model's geometric dependence on the spine.
Leveraging Unlabeled Data for 3D Medical Image Segmentation through Self-Supervised Contrastive Learning
Current 3D semi-supervised segmentation methods face significant challenges such as limited consideration of contextual information and the inability to generate reliable pseudo-labels for effective unsupervised data use.
Overcoming Data Scarcity in Biomedical Imaging with a Foundational Multi-Task Model
The UMedPT foundational model outperformed ImageNet pretraining and the previous state-of-the-art models.
Foundational Models in Medical Imaging: A Comprehensive Survey and Future Vision
Foundation models, large-scale, pre-trained deep-learning models adapted to a wide range of downstream tasks have gained significant interest lately in various deep-learning problems undergoing a paradigm shift with the rise of these models.
INCODE: Implicit Neural Conditioning with Prior Knowledge Embeddings
INCODE comprises a harmonizer network and a composer network, where the harmonizer network dynamically adjusts key parameters of the activation function.
Semi-supervised Instance Segmentation with a Learned Shape Prior
To date, most instance segmentation approaches are based on supervised learning that requires a considerable amount of annotated object contours as training ground truth.
SortedAP: Rethinking evaluation metrics for instance segmentation
Designing metrics for evaluating instance segmentation revolves around comprehensively considering object detection and segmentation accuracy.
Self-supervised Semantic Segmentation: Consistency over Transformation
Accurate medical image segmentation is of utmost importance for enabling automated clinical decision procedures.
Laplacian-Former: Overcoming the Limitations of Vision Transformers in Local Texture Detection
Vision Transformer (ViT) models have demonstrated a breakthrough in a wide range of computer vision tasks.
Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation
To address these challenges, we introduce the concept of \textbf{Deformable Large Kernel Attention (D-LKA Attention)}, a streamlined attention mechanism employing large convolution kernels to fully appreciate volumetric context.
Unlocking Fine-Grained Details with Wavelet-based High-Frequency Enhancement in Transformers
Furthermore, to intensify the importance of the boundary information, we impose an additional attention map by creating a Gaussian pyramid on top of the HF components.
DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion Delineation
Skin lesion segmentation plays a critical role in the early detection and accurate diagnosis of dermatological conditions.
Ranked #1 on
Lesion Segmentation
on HAM10000
Implicit Neural Representation in Medical Imaging: A Comparative Survey
Recognizing the potential of INRs beyond these domains, this survey aims to provide a comprehensive overview of INR models in the field of medical imaging.
Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free Approach
This approach eliminates the need for manual annotation, making it particularly suitable for medical images with limited annotated data.
Ranked #17 on
Few-Shot Semantic Segmentation
on FSS-1000 (5-shot)
A Deep Learning-based in silico Framework for Optimization on Retinal Prosthetic Stimulation
The pre-trained retinal implant model is also a U-Net, which is trained to mimic the biomimetic perceptual model implemented in pulse2percept.
Enhancing Medical Image Segmentation with TransCeption: A Multi-Scale Feature Fusion Approach
(3) In contrast to a bridge that only contains token-wise self-attention, we propose a Dual Transformer Bridge that also includes channel-wise self-attention to exploit correlations between scales at different stages from a dual perspective.
Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review
The remarkable performance of the Transformer architecture in natural language processing has recently also triggered broad interest in Computer Vision.
DAE-Former: Dual Attention-guided Efficient Transformer for Medical Image Segmentation
Transformers have recently gained attention in the computer vision domain due to their ability to model long-range dependencies.
Medical Image Segmentation Review: The success of U-Net
U-Net is the most widespread image segmentation architecture due to its flexibility, optimized modular design, and success in all medical image modalities.
Diffusion Models for Medical Image Analysis: A Comprehensive Survey
Then, we provide a systematic taxonomy of diffusion models in the medical domain and propose a multi-perspective categorization based on their application, imaging modality, organ of interest, and algorithms.
Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentation
We argue that the classical concatenation operation utilized in the skip connection path can be further improved by incorporating an attention mechanism.
Instance Segmentation of Dense and Overlapping Objects via Layering
Instance segmentation aims to delineate each individual object of interest in an image.
TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation
Especially, deep neural networks based on seminal architectures such as U-shaped models with skip-connections or atrous convolution with pyramid pooling have been tailored to a wide range of medical image analysis tasks.
TransNorm: Transformer Provides a Strong Spatial Normalization Mechanism for a Deep Segmentation Model
In the past few years, convolutional neural networks (CNNs), particularly U-Net, have been the prevailing technique in the medical image processing era.
HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation
In this paper, we propose HiFormer, a novel method that efficiently bridges a CNN and a transformer for medical image segmentation.
SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities
Gliomas are one of the most prevalent types of primary brain tumours, accounting for more than 30\% of all cases and they develop from the glial stem or progenitor cells.
Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach
Accurate and automatic segmentation of intervertebral discs from medical images is a critical task for the assessment of spine-related diseases such as osteoporosis, vertebral fractures, and intervertebral disc herniation.
Medical Image Segmentation on MRI Images with Missing Modalities: A Review
Dealing with missing modalities in Magnetic Resonance Imaging (MRI) and overcoming their negative repercussions is considered a hurdle in biomedical imaging.
Contextual Attention Network: Transformer Meets U-Net
Then, they emphasize the informative regions while taking into account the long-range contextual dependency derived by the Transformer module.
Improving Unsupervised Stain-To-Stain Translation using Self-Supervision and Meta-Learning
In digital pathology, many image analysis tasks are challenged by the need for large and time-consuming manual data annotations to cope with various sources of variability in the image domain.
Multiscale Softmax Cross Entropy for Fovea Localization on Color Fundus Photography
Fovea localization is one of the most popular tasks in ophthalmic medical image analysis, where the coordinates of the center point of the macula lutea, i. e. fovea centralis, should be calculated based on color fundus images.
The information content of brain states is explained by structural constraints on state energetics
These activity patterns define global brain states and contain information in accordance with their expected probability of occurrence.
High-throughput Phenotyping of Nematode Cysts
The beet cyst nematode (BCN) Heterodera schachtii is a plant pest responsible for crop loss on a global scale.
Transfer Learning Gaussian Anomaly Detection by Fine-tuning Representations
In our work, we propose a new method to overcome catastrophic forgetting and thus successfully fine-tune pre-trained representations for AD in the transfer learning setting.
The Medical Segmentation Decathlon
Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem.
Improving Pixel Embedding Learning through Intermediate Distance Regression Supervision for Instance Segmentation
A distance regression module is incorporated into our architecture to generate seeds for fast clustering.
Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection
We follow an alternative approach and demonstrate that deep feature representations learned by discriminative models on large natural image datasets are well suited to describe normality and detect even subtle anomalies in a transfer learning setting.
Ranked #83 on
Anomaly Detection
on MVTec AD
AutoML Segmentation for 3D Medical Image Data: Contribution to the MSD Challenge 2018
Fueled by recent advances in machine learning, there has been tremendous progress in the field of semantic segmentation for the medical image computing community.
An Asymmetric Cycle-Consistency Loss for Dealing with Many-to-One Mappings in Image Translation: A Study on Thigh MR Scans
Generative adversarial networks using a cycle-consistency loss facilitate unpaired training of image-translation models and thereby exhibit a very high potential in manifold medical applications.
Instance Segmentation of Biomedical Images with an Object-aware Embedding Learned with Local Constraints
The network is trained to output embedding vectors of similar directions for pixels from the same object, while adjacent objects are orthogonal in the embedding space, which effectively avoids the fusion of objects in a crowd.
MixNet: Multi-modality Mix Network for Brain Segmentation
Automated brain structure segmentation is important to many clinical quantitative analysis and diagnoses.
Radiomic Feature Stability Analysis based on Probabilistic Segmentations
From these segmentations, we extract a high number of plausible feature vectors for each lung tumor and analyze feature variance with respect to the segmentations.
Multi Scale Curriculum CNN for Context-Aware Breast MRI Malignancy Classification
Achieving an AUROC of 0. 89, we compare the performance of our approach to Mask R-CNN and Retina U-Net as well as a radiologist.
Super-realtime facial landmark detection and shape fitting by deep regression of shape model parameters
We present a method for highly efficient landmark detection that combines deep convolutional neural networks with well established model-based fitting algorithms.
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Image-based Survival Analysis for Lung Cancer Patients using CNNs
Traditional survival models such as the Cox proportional hazards model are typically based on scalar or categorical clinical features.
Spherical Harmonic Residual Network for Diffusion Signal Harmonization
For this purpose, a training database is required, which consist of the same subjects, scanned on different scanners.
DELIMIT PyTorch - An extension for Deep Learning in Diffusion Imaging
DELIMIT is a framework extension for deep learning in diffusion imaging, which extends the basic framework PyTorch towards spherical signals.
Unsupervisedly Training GANs for Segmenting Digital Pathology with Automatically Generated Annotations
Recently, generative adversarial networks exhibited excellent performances in semi-supervised image analysis scenarios.
Context-based Normalization of Histological Stains using Deep Convolutional Features
While human observers are able to cope with variations in color and appearance of histological stains, digital pathology algorithms commonly require a well-normalized setting to achieve peak performance, especially when a limited amount of labeled data is available.
CNN Cascades for Segmenting Whole Slide Images of the Kidney
Due to the increasing availability of whole slide scanners facilitating digitization of histopathological tissue, there is a strong demand for the development of computer based image analysis systems.
Recycle deep features for better object detection
Aiming at improving the performance of existing detection algorithms developed for different applications, we propose a region regression-based multi-stage class-agnostic detection pipeline, whereby the existing algorithms are employed for providing the initial detection proposals.
