Robotic ultrasound imaging: State-of-the-art and future perspectives
Ultrasound (US) is one of the most widely used modalities for clinical intervention and
diagnosis due to the merits of providing non-invasive, radiation-free, and real-time images …
diagnosis due to the merits of providing non-invasive, radiation-free, and real-time images …
Understanding metric-related pitfalls in image analysis validation
Validation metrics are key for tracking scientific progress and bridging the current chasm
between artificial intelligence research and its translation into practice. However, increasing …
between artificial intelligence research and its translation into practice. However, increasing …
Segment anything in medical images
Medical image segmentation is a critical component in clinical practice, facilitating accurate
diagnosis, treatment planning, and disease monitoring. However, existing methods, often …
diagnosis, treatment planning, and disease monitoring. However, existing methods, often …
The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification
Binary classification is a common task for which machine learning and computational
statistics are used, and the area under the receiver operating characteristic curve (ROC …
statistics are used, and the area under the receiver operating characteristic curve (ROC …
CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII
Even though radiomics can hold great potential for supporting clinical decision-making, its
current use is mostly limited to academic research, without applications in routine clinical …
current use is mostly limited to academic research, without applications in routine clinical …
The multimodality cell segmentation challenge: toward universal solutions
Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images.
Existing cell segmentation methods are often tailored to specific modalities or require …
Existing cell segmentation methods are often tailored to specific modalities or require …
UNETR++: delving into efficient and accurate 3D medical image segmentation
Owing to the success of transformer models, recent works study their applicability in 3D
medical segmentation tasks. Within the transformer models, the self-attention mechanism is …
medical segmentation tasks. Within the transformer models, the self-attention mechanism is …
Segmentation metric misinterpretations in bioimage analysis
Quantitative evaluation of image segmentation algorithms is crucial in the field of bioimage
analysis. The most common assessment scores, however, are often misinterpreted and …
analysis. The most common assessment scores, however, are often misinterpreted and …
Unleashing the strengths of unlabeled data in pan-cancer abdominal organ quantification: the flare22 challenge
Quantitative organ assessment is an essential step in automated abdominal disease
diagnosis and treatment planning. Artificial intelligence (AI) has shown great potential to …
diagnosis and treatment planning. Artificial intelligence (AI) has shown great potential to …
Mitigating bias in radiology machine learning: 3. Performance metrics
The increasing use of machine learning (ML) algorithms in clinical settings raises concerns
about bias in ML models. Bias can arise at any step of ML creation, including data handling …
about bias in ML models. Bias can arise at any step of ML creation, including data handling …