AIMC Topic: Image Interpretation, Computer-Assisted

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Deep CNNs for glioma grading on conventional MRIs: Performance analysis, challenges, and future directions.

Mathematical biosciences and engineering : MBE
The increasing global incidence of glioma tumors has raised significant healthcare concerns due to their high mortality rates. Traditionally, tumor diagnosis relies on visual analysis of medical imaging and invasive biopsies for precise grading. As a...

A novel lightweight deep learning approach for simultaneous optic cup and optic disc segmentation in glaucoma detection.

Mathematical biosciences and engineering : MBE
Glaucoma is a chronic neurodegenerative disease that can result in irreversible vision loss if not treated in its early stages. The cup-to-disc ratio is a key criterion for glaucoma screening and diagnosis, and it is determined by dividing the area o...

Robust Visual Identification of Under-resourced Dermatological Diagnoses with Classifier-Steered Background Masking.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Collecting images of rare dermatological diseases for machine learning detection applications is a costly, laborious task. It is difficult to collect enough images of these diagnoses to avoid the risk of low accuracy "in the wild". One of the sources...

PathSAM: Enhancing Oral Cancer Detection with Advanced Segmentation and Explainability.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Building on the success of the Segment Anything Model (SAM) in image segmentation, "PathSAM: SAM for Pathological Images in Oral Cancer Detection" addresses the unique challenges associated with diagnosing oral cancer. Although SAM is versatile, its ...

Prediction of T Stage of Rectal Cancer After Neoadjuvant Therapy by Multi-Parameter Magnetic Resonance Radiomics Based on Machine Learning Algorithms.

Technology in cancer research & treatment
INTRODUCTION: Since the response of patients with rectal cancer (RC) to neoadjuvant therapy is highly variable, there is an urgent need to develop accurate methods to predict the post-treatment T (pT) stage. The purpose of this study was to evaluate ...

A Hybrid 2D Gaussian Filter and Deep Learning Approach with Visualization of Class Activation for Automatic Lung and Colon Cancer Diagnosis.

Technology in cancer research & treatment
Cancer is a significant public health issue due to its high prevalence and lethality, particularly lung and colon cancers, which account for over a quarter of all cancer cases. This study aims to enhance the detection rate of lung and colon cancer by...

Machine Learning and Radiomics in Gliomas.

Advances in experimental medicine and biology
The integration of machine learning (ML) and radiomics is emerging as a pivotal advancement in glioma research, offering novel insights into the diagnosis, prognosis, and treatment of these complex tumors. Radiomics involves the extraction of a multi...

Meta-transfer Learning for Brain Tumor Segmentation: Within and Beyond Glioma.

Advances in experimental medicine and biology
In recent years, numerous algorithms have emerged for the segmentation of brain tumors, propelled by both the advancements of deep learning techniques and the influential open benchmark set by the BraTS challenge. This chapter provides an overview of...

Artificial Intelligence in Brain Tumors.

Advances in experimental medicine and biology
The introduction of "intelligent machines" goes back to Alan Turing in the 1940s. Artificial intelligence (AI) is a broad umbrella covering different methodologies, such as machine learning and deep learning. Deep learning, characterized by multilaye...