AIMC Topic: Image Interpretation, Computer-Assisted

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Leveraging domain knowledge for synthetic ultrasound image generation: a novel approach to rare disease AI detection.

International journal of computer assisted radiology and surgery
PURPOSE: This study explores the use of deep generative models to create synthetic ultrasound images for the detection of hemarthrosis in hemophilia patients. Addressing the challenge of sparse datasets in rare disease diagnostics, the study aims to ...

Attention incorporated network for sharing low-rank, image and k-space information during MR image reconstruction to achieve single breath-hold cardiac Cine imaging.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Cardiac Cine Magnetic Resonance Imaging (MRI) provides an accurate assessment of heart morphology and function in clinical practice. However, MRI requires long acquisition times, with recent deep learning-based methods showing great promise to accele...

Role of Artificial Intelligence for Endoscopic Ultrasound.

Gastrointestinal endoscopy clinics of North America
Endoscopic ultrasound (EUS) is widely used for the diagnosis of biliopancreatic and gastrointestinal tract diseases, but it is one of the most operator-dependent endoscopic techniques, requiring a long and complex learning curve. The role of artifici...

BCD-TransNet: Automatic breast cancer detection and classification using transfer learning approach.

Technology and health care : official journal of the European Society for Engineering and Medicine
Breast Cancer (BC) is a predominant form of cancer diagnosed in women and one of the deadliest diseases. The important cause of death owing to the cancer amongst women is BC. However, the existing ML techniques are very challenge evaluate the perform...

A wrapper method for finding optimal subset of multimodal Magnetic Resonance Imaging sequences for ischemic stroke lesion segmentation.

Computers in biology and medicine
Multimodal data, while being information-rich, contains complementary as well as redundant information. Depending on the target problem some modalities are more informative and thus relevant for decision-making. Identifying the optimal subset of moda...

Learnable color space conversion and fusion for stain normalization in pathology images.

Medical image analysis
Variations in hue and contrast are common in H&E-stained pathology images due to differences in slide preparation across various institutions. Such stain variations, while not affecting pathologists much in diagnosing the biopsy, pose significant cha...

CircWaveDL: Modeling of optical coherence tomography images based on a new supervised tensor-based dictionary learning for classification of macular abnormalities.

Artificial intelligence in medicine
Modeling Optical Coherence Tomography (OCT) images is crucial for numerous image processing applications and aids ophthalmologists in the early detection of macular abnormalities. Sparse representation-based models, particularly dictionary learning (...

Ensemble learning-based radiomics model for discriminating brain metastasis from glioblastoma.

European journal of radiology
OBJECTIVE: Differentiating between brain metastasis (BM) and glioblastoma (GBM) preoperatively is challenging due to their similar imaging features on conventional brain MRI. This study aimed to enhance diagnostic accuracy through a machine learning ...

A feature fusion method based on radiomic features and revised deep features for improving tumor prediction in ultrasound images.

Computers in biology and medicine
BACKGROUND: Radiomic features and deep features are both vitally helpful for the accurate prediction of tumor information in breast ultrasound. However, whether integrating radiomic features and deep features can improve the prediction performance of...