Papers Lesion Detection
“Lesion Detection” 태그가 달린 논문 257편 · 필터 해제
Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI
The segmentation of metastatic bone disease (MBD) in whole-body MRI (WB-MRI) is a challenging problem. Due to varying appearances and anatomical locations of lesions, ambiguous boundaries, and severe class imbalance, obt…
AnatomyInductive BiasLesion DetectionSelf-Supervised LearningMAMBO: High-Resolution Generative Approach for Mammography Images
Mammography is the gold standard for the detection and diagnosis of breast cancer. This procedure can be significantly enhanced with Artificial Intelligence (AI)-based software, which assists radiologists in identifying …
Anomaly DetectionImage GenerationLesion DetectionSuper-ResolutionConfLUNet: Multiple sclerosis lesion instance segmentation in presence of confluent lesions
Accurate lesion-level segmentation on MRI is critical for multiple sclerosis (MS) diagnosis, prognosis, and disease monitoring. However, current evaluation practices largely rely on semantic segmentation post-processed w…
Instance SegmentationLesion DetectionPrognosisSegmentation+1From What to How: Attributing CLIP's Latent Components Reveals Unexpected Semantic Reliance
Transformer-based CLIP models are widely used for text-image probing and feature extraction, making it relevant to understand the internal mechanisms behind their predictions. While recent works show that Sparse Autoenco…
Lesion DetectionA Smart Healthcare System for Monkeypox Skin Lesion Detection and Tracking
Monkeypox is a viral disease characterized by distinctive skin lesions and has been reported in many countries. The recent global outbreak has emphasized the urgent need for scalable, accessible, and accurate diagnostic …
Binary ClassificationDiagnosticLesion DetectionTransfer LearningMoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images
Natural images exhibit label diversity (clean vs. noisy) in noisy-labeled image classification and prevalence diversity (abundant vs. sparse) in long-tailed image classification. Similarly, medical images in universal le…
Diversityimage-classificationImage ClassificationLesion DetectionDiscovering Pathology Rationale and Token Allocation for Efficient Multimodal Pathology Reasoning
Multimodal pathological image understanding has garnered widespread interest due to its potential to improve diagnostic accuracy and enable personalized treatment through integrated visual and textual data. However, exis…
Computational EfficiencyDiagnosticLesion DetectionQuestion Answering+1A Comprehensive Review of Techniques, Algorithms, Advancements, Challenges, and Clinical Applications of Multi-modal Medical Image Fusion for Improved Diagnosis
Multi-modal medical image fusion (MMIF) is increasingly recognized as an essential technique for enhancing diagnostic precision and facilitating effective clinical decision-making within computer-aided diagnosis systems.…
AnatomyComputational EfficiencyDiagnosticFederated Learning+2SCOPE-MRI: Bankart Lesion Detection as a Case Study in Data Curation and Deep Learning for Challenging Diagnoses
While deep learning has shown strong performance in musculoskeletal imaging, existing work has largely focused on pathologies where diagnosis is not a clinical challenge, leaving more difficult problems underexplored, su…
DiagnosticLesion DetectionSpecificityMediAug: Exploring Visual Augmentation in Medical Imaging
Data augmentation is essential in medical imaging for improving classification accuracy, lesion detection, and organ segmentation under limited data conditions. However, two significant challenges remain. First, a pronou…
Brain Tumor ClassificationData AugmentationLesion DetectionOrgan SegmentationClass Imbalance Correction for Improved Universal Lesion Detection and Tagging in CT
Radiologists routinely detect and size lesions in CT to stage cancer and assess tumor burden. To potentially aid their efforts, multiple lesion detection algorithms have been developed with a large public dataset called …
Lesion DetectionTAGU2AD: Uncertainty-based Unsupervised Anomaly Detection Framework for Detecting T2 Hyperintensity in MRI Spinal Cord
T2 hyperintensities in spinal cord MR images are crucial biomarkers for conditions such as degenerative cervical myelopathy. However, current clinical diagnoses primarily rely on manual evaluation. Deep learning methods …
Anomaly DetectionLesion DetectionUnsupervised Anomaly DetectionAA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIP
Anomaly detection (AD) identifies outliers for applications like defect and lesion detection. While CLIP shows promise for zero-shot AD tasks due to its strong generalization capabilities, its inherent Anomaly-Unawarenes…
Anomaly DetectionAnomaly LocalizationLesion Detectionzero-shot anomaly detectionThe iToBoS dataset: skin region images extracted from 3D total body photographs for lesion detection
Artificial intelligence has significantly advanced skin cancer diagnosis by enabling rapid and accurate detection of malignant lesions. In this domain, most publicly available image datasets consist of single, isolated s…
BenchmarkingDiagnosticLesion DetectionWaveNet-SF: A Hybrid Network for Retinal Disease Detection Based on Wavelet Transform in the Spatial-Frequency Domain
Retinal diseases are a leading cause of vision impairment and blindness, with timely diagnosis being critical for effective treatment. Optical Coherence Tomography (OCT) has become a standard imaging modality for retinal…
Lesion DetectionMedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis
As deep learning models gain attraction in medical data, ensuring transparent and trustworthy decision-making is essential. In skin cancer diagnosis, while advancements in lesion detection and classification have improve…
DiagnosticLesion DetectionMedical Image AnalysisImproved joint modelling of breast cancer radiomics features and hazard by image registration aided longitudinal CT data
Patients with metastatic breast cancer (mBC) undergo continuous medical imaging during treatment, making accurate lesion detection and monitoring over time critical for clinical decisions. Predicting drug response from p…
Image RegistrationLesion DetectionSurvival AnalysisVariable SelectionRadHop-Net: A Lightweight Radiomics-to-Error Regression for False Positive Reduction In MRI Prostate Cancer Detection
Clinically significant prostate cancer (csPCa) is a leading cause of cancer death in men, yet it has a high survival rate if diagnosed early. Bi-parametric MRI (bpMRI) reading has become a prominent screening test for cs…
DiagnosticLesion DetectionQSM-RimDS: A detection and segmentation tool for paramagnetic rim lesions in multiple sclerosis
Paramagnetic rim lesions (PRLs) are an emerging biomarker in multiple sclerosis (MS). Manual identification and rim segmentation of PRLs on quantitative susceptibility mapping (QSM) images are time-consuming. Deep learni…
Lesion DetectionSegmentationRevisiting Lesion Tracking in 3D Total Body Photography
Melanoma is the most deadly form of skin cancer. Tracking the evolution of nevi and detecting new lesions across the body is essential for the early detection of melanoma. Despite prior work on longitudinal tracking of s…
Lesion Detection