AIMC Topic: Image Interpretation, Computer-Assisted

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DeepHybrid-CNN: A hybrid approach for pre-processing of skin cancer images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
In the current technological era, digital imaging is ubiquitous, and it serves a crucial purpose in the realm of medical research. Skin cancer is one of the most common types of cancer, and its early diagnosis is essential to reduce the mortality rat...

High-efficiency spatially guided learning network for lymphoblastic leukemia detection in bone marrow microscopy images.

Computers in biology and medicine
Leukemia is a hematologic tumor that proliferates in bone marrow and seriously affects the survival of patients. Early and accurate diagnosis is crucial for effective leukemia treatment. Traditional diagnostic methods rely on experts' subjective anal...

IgCONDA-PET: Weakly-supervised PET anomaly detection using implicitly-guided attention-conditional counterfactual diffusion modeling - a multi-center, multi-cancer, and multi-tracer study.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Minimizing the need for pixel-level annotated data to train PET lesion detection and segmentation networks is highly desired and can be transformative, given time and cost constraints associated with expert annotations. Current unsupervised or weakly...

TA-SSM net: tri-directional attention and structured state-space model for enhanced MRI-Based diagnosis of Alzheimer's disease and mild cognitive impairment.

BMC medical imaging
Early diagnosis of Alzheimer's disease (AD) and its precursor, mild cognitive impairment (MCI), is critical for effective prevention and treatment. Computer-aided diagnosis using magnetic resonance imaging (MRI) provides a cost-effective and objectiv...

A brain tumor segmentation enhancement in MRI images using U-Net and transfer learning.

BMC medical imaging
This paper presents a novel transfer learning approach for segmenting brain tumors in Magnetic Resonance Imaging (MRI) images. Using Fluid-Attenuated Inversion Recovery (FLAIR) abnormality segmentation masks and MRI scans from The Cancer Genome Atlas...

A successive framework for brain tumor interpretation using Yolo variants.

Scientific reports
Accurate identification and segmentation of brain tumors in Magnetic Resonance Imaging (MRI) images are critical for timely diagnosis and treatment. MRI is frequently used to diagnose these disorders; however medical professionals find it challenging...

M3-Net++: A multi-scale, multi-level, multi-stream network for nuclei segmentation in breast cancer histopathology using hierarchical context extraction and hybrid loss optimization.

Computers in biology and medicine
Breast cancer remains a leading cause of morbidity and mortality worldwide. Histopathology, particularly the analysis of nuclear morphology in tissue samples, is critical for diagnosing and understanding the progression of breast cancer. Accurate nuc...

K-Means Clustering and Classification of Breast Cancer Images Using Histogram of Oriented Gradients Features and Convolutional Neural Network Models: Diagnostic Image Analysis Study.

JMIR formative research
BACKGROUND: Breast cancer has proven to be the most common type of cancer among females around the world. However, mortality rates can be reduced if it is diagnosed at the initial stages. Interpretation made by an expert is required by conventional d...

Unveiling key pathomic features for automated diagnosis and Gleason grade estimation in prostate cancer.

BMC medical imaging
BACKGROUND: Recent advances in histology scanning technology and Artificial Intelligence (AI) offer great opportunities to support cancer diagnosis. The inability to interpret the extracted features and model predictions is one of the major issues li...

CVT-HNet: a fusion model for recognizing perianal fistulizing Crohn's disease based on CNN and ViT.

BMC medical imaging
BACKGROUND: Accurate identification of anal fistulas is essential, as it directly impacts the severity of subsequent perianal infections, prognostic indicators, and overall treatment outcomes. Traditional manual recognition methods are inefficient. I...