Medical Image Analysis

Papers
(The H4-Index of Medical Image Analysis is 73. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-05-01 to 2024-05-01.)
ArticleCitations
Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning679
Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation487
Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation356
Deep neural network models for computational histopathology: A survey334
Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis325
CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation309
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis288
PadChest: A large chest x-ray image dataset with multi-label annotated reports279
A survey on active learning and human-in-the-loop deep learning for medical image analysis250
Recent advances and clinical applications of deep learning in medical image analysis249
Loss odyssey in medical image segmentation247
Accurate brain age prediction with lightweight deep neural networks240
The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge219
Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results213
BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis212
Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks210
Deep learning for chest X-ray analysis: A survey199
Transformers in medical imaging: A survey195
FAT-Net: Feature adaptive transformers for automated skin lesion segmentation193
The Liver Tumor Segmentation Benchmark (LiTS)192
Applications of deep learning in fundus images: A review185
CS2-Net: Deep learning segmentation of curvilinear structures in 169
Skin lesion segmentation via generative adversarial networks with dual discriminators162
Autoencoders for unsupervised anomaly segmentation in brain MR images: A comparative study158
A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging157
Multi-task learning for segmentation and classification of tumors in 3D automated breast ultrasound images153
Faster Mean-shift: GPU-accelerated clustering for cosine embedding-based cell segmentation and tracking151
Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation144
Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan144
Models Genesis141
TransMorph: Transformer for unsupervised medical image registration138
Boundary loss for highly unbalanced segmentation131
A survey on incorporating domain knowledge into deep learning for medical image analysis131
Dual-branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images126
SCS-Net: A Scale and Context Sensitive Network for Retinal Vessel Segmentation122
Surgical data science – from concepts toward clinical translation116
Head and neck tumor segmentation in PET/CT: The HECKTOR challenge114
VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images112
AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia108
CycleMorph: Cycle consistent unsupervised deformable image registration102
Supervised learning with cyclegan for low-dose FDG PET image denoising102
Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency101
Federated learning for computational pathology on gigapixel whole slide images100
Global guidance network for breast lesion segmentation in ultrasound images97
HookNet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images96
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization95
The reliability of a deep learning model in clinical out-of-distribution MRI data: A multicohort study93
Transformer-based unsupervised contrastive learning for histopathological image classification92
Cellular community detection for tissue phenotyping in colorectal cancer histology images92
EndoSLAM dataset and an unsupervised monocular visual odometry and depth estimation approach for endoscopic videos91
Deep reinforcement learning in medical imaging: A literature review91
ResGANet: Residual group attention network for medical image classification and segmentation90
Interpretable deep learning systems for multi-class segmentation and classification of non-melanoma skin cancer88
COVID-AL: The diagnosis of COVID-19 with deep active learning87
Fine-Tuning and training of densenet for histopathology image representation using TCGA diagnostic slides86
BS-Net: Learning COVID-19 pneumonia severity on a large chest X-ray dataset86
Surgical spectral imaging85
Rubik’s Cube+: A self-supervised feature learning framework for 3D medical image analysis85
Score-based diffusion models for accelerated MRI85
Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy85
A review of machine learning methods for retinal blood vessel segmentation and artery/vein classification85
Ms RED: A novel multi-scale residual encoding and decoding network for skin lesion segmentation82
Applying deep learning in digital breast tomosynthesis for automatic breast cancer detection: A review81
Triple attention learning for classification of 14 thoracic diseases using chest radiography80
A survey on medical image analysis in diabetic retinopathy80
Test-time adaptable neural networks for robust medical image segmentation79
A Framework for the generation of digital twins of cardiac electrophysiology from clinical 12-leads ECGs77
Triple U-net: Hematoxylin-aware nuclei segmentation with progressive dense feature aggregation77
Semi-supervised medical image segmentation via a tripled-uncertainty guided mean teacher model with contrastive learning75
End-to-end prostate cancer detection in bpMRI via 3D CNNs: Effects of attention mechanisms, clinical priori and decoupled false positive reduction75
Analysis of the ISIC image datasets: Usage, benchmarks and recommendations74
MSCS-DeepLN: Evaluating lung nodule malignancy using multi-scale cost-sensitive neural networks73
Segment anything model for medical image analysis: An experimental study73
mustGAN: multi-stream Generative Adversarial Networks for MR Image Synthesis73
0.03714394569397