AIMC Topic: Image Interpretation, Computer-Assisted

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Extended Technical and Clinical Validation of Deep Learning-Based Brainstem Segmentation for Application in Neurodegenerative Diseases.

Human brain mapping
Disorders of the central nervous system, including neurodegenerative diseases, frequently affect the brainstem and can present with focal atrophy. This study aimed to (1) optimize deep learning-based brainstem segmentation for a wide range of patholo...

Thinking like a pathologist: Morphologic approach to hepatobiliary tumors by ChatGPT.

American journal of clinical pathology
OBJECTIVES: This research aimed to evaluate the effectiveness of ChatGPT in accurately diagnosing hepatobiliary tumors using histopathologic images.

DEFIF-Net: A lightweight dual-encoding feature interaction fusion network for medical image segmentation.

PloS one
Medical image segmentation plays a crucial role in computer-aided diagnosis. By segmenting pathological tissues in medical images, doctors can observe anatomical structures more clearly, thereby achieving more accurate disease diagnoses. However, exi...

Breast cancer pathology image recognition based on convolutional neural network.

PloS one
This study presents a convolutional neural network (CNN)-based method for the classification and recognition of breast cancer pathology images. It aims to solve the problems existing in traditional pathological tissue analysis methods, such as time-c...

OS-DETR: End-to-end brain tumor detection framework based on orthogonal channel shuffle networks.

PloS one
OrthoNets use the Gram-Schmidt process to achieve orthogonality among filters but do not impose constraints on the internal orthogonality of individual filters. To reduce the risk of overfitting, especially in scenarios with limited data such as medi...

Attention-aware Deep Learning Models for Dermoscopic Image Classification for Skin Disease Diagnosis.

Current medical imaging
BACKGROUND: The skin, being the largest organ in the human body, plays a vital protective role. Skin lesions are changes in the appearance of the skin, such as bumps, sores, lumps, patches, and discoloration. If not identified and treated promptly, s...

Subject-Based Transfer Learning in Longitudinal Multiple Sclerosis Lesion Segmentation.

Journal of neuroimaging : official journal of the American Society of Neuroimaging
BACKGROUND AND PURPOSE: Accurate and consistent lesion segmentation from magnetic resonance imaging is required for longitudinal multiple sclerosis (MS) data analysis. In this work, we propose two new transfer learning-based pipelines to improve segm...

Deep Learning-assisted Diagnosis of Extrahepatic Common Bile Duct Obstruction Using MRCP Imaging and Clinical Parameters.

Current medical imaging
BACKGROUND: Extrahepatic Common Bile Duct Obstruction (EHBDO) is a serious condition that requires accurate diagnosis for effective treatment. Magnetic Resonance Cholangiopancreatography (MRCP) is a widely used noninvasive imaging technique for visua...

Deep Learning to Simulate Contrast-Enhanced MRI for Evaluating Suspected Prostate Cancer.

Radiology
Background Multiparametric MRI, including contrast-enhanced sequences, is recommended for evaluating suspected prostate cancer, but concerns have been raised regarding potential contrast agent accumulation and toxicity. Purpose To evaluate the feasib...