AIMC Topic: Image Interpretation, Computer-Assisted

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Weakly Supervised Breast Ultrasound Image Segmentation Based on Image Selection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Automatic segmentation in Breast Ultrasound (BUS) imaging is vital to BUS computer-aided diagnostic systems. Fully supervised learning approaches can attain high accuracy, yet they depend on pixel-level annotations that are challenging to obtain. As ...

Channel Fitting Network for Retinal Lesion Segmentation from OCT Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Retinal lesion is a cause of age-related macular degeneration that poses a big threat to elderly population. The accurate detection and segmentation of retinal lesions benefits the early diagnosis of age-related macular degeneration and monitoring of...

Enhancing Choroidal Nevus Position Identification through CNN-Based Segmentation of Eye Fundus Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Diagnosing choroidal nevus in color fundus images is challenging for clinicians not regularly practicing it. Machine learning (ML) has proven effective in detecting and analyzing such abnormalities with high accuracy and efficiencyThis research is pa...

A comparison between Deep Learning architectures for the assessment of breast tumor segmentation using VSI ultrasound protocol.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Automatic breast tumor ultrasound segmentation is one of the most critical components in the development of tools for breast cancer diagnosis. Several deep learning algorithms have been tested with public and private datasets but none of them has bee...

Hard example mining in Multi-Instance Learning for Whole-Slide Image Classification.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Multiple instance learning(MIL) has shown superior performance in the classification of whole-slide images(WSIs). The implementation of multiple instance learning for WSI classification typically involves two components, i.e., a feature extractor, wh...

Lesion Segmentation in Skin Cancer Images using Fusion Model via Deep Learning Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Skin cancer, one of the most prevalent and life-threatening cancers globally, has become a focus of deep learning applications due to its significant impact on diagnostic accuracy. This research specifically addresses lesion segmentation in skin canc...

Selection of Dataframes Presenting Glioma from Magnetic Resonance Images: a Deep Learning Approach.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Due to its complexity and time-consuming nature, identifying gliomas at the Magnetic Resonance Imaging (MRI) slice-level before segmentation could assist clinicians in minimizing the time required for this procedure. In the literature, many studies p...

Ensemble Distillation of Divergent Opinions for Robust Pathological Image Classification.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The construction of highly accurate deep neural networks (DNNs) requires consistent labeled data. However, there are numerous cases wherein the ground truth is not uniquely determined, even for the same data, owing to different interpretations depend...

Weakly Supervised Deep Learning in Radiology.

Radiology
Deep learning (DL) is currently the standard artificial intelligence tool for computer-based image analysis in radiology. Traditionally, DL models have been trained with strongly supervised learning methods. These methods depend on reference standard...

Potential Role of Generative Adversarial Networks in Enhancing Brain Tumors.

JCO clinical cancer informatics
PURPOSE: Contrast enhancement is necessary for visualizing, diagnosing, and treating brain tumors. Through this study, we aimed to examine the potential role of general adversarial neural networks in generating artificial intelligence-based enhanceme...