AIMC Topic: Image Interpretation, Computer-Assisted

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A Deep-Learning Framework for Ovarian Cancer Subtype Classification Using Whole Slide Images.

Studies in health technology and informatics
Ovarian cancer, a leading cause of cancer-related deaths among women, comprises distinct subtypes each requiring different treatment approaches. This paper presents a deep-learning framework for classifying ovarian cancer subtypes using Whole Slide I...

Leveraging Vision Transformers in Multimodal Models for Retinal OCT Analysis.

Studies in health technology and informatics
Optical Coherence Tomography (OCT) has become an indispensable imaging modality in ophthalmology, providing high-resolution cross-sectional images of the retina. Accurate classification of OCT images is crucial for diagnosing retinal diseases such as...

RepSE-CBAMNet: A Hybrid Attention-Enhanced CNN for Brain Tumor Detection.

Studies in health technology and informatics
The effective detection of brain tumors is closely linked to their timely diagnosis and treatment which can help in the prevention of deaths and in improving the quality of life. The objective of this paper is to present an enhanced YOLO (You Look On...

Bias Detection in Histology Images Using Explainable AI and Image Darkness Assessment.

Studies in health technology and informatics
The study underscores the importance of addressing biases in medical AI models to improve fairness, generalizability, and clinical utility. In this paper, we present a novel framework that combines Explainable AI (XAI) with image darkness assessment ...

Automatic Segmentation of Histopathological Glioblastoma Whole-Slide Images Utilizing MONAI.

Studies in health technology and informatics
Manual segmentation of histopathological images is both resource-intensive and prone to human error, particularly when dealing with challenging tumor types like Glioblastoma (GBM), an aggressive and highly heterogeneous brain tumor. The fuzzy borders...

Understanding Stain Separation Improves Cross-Scanner Adenocarcinoma Segmentation with Joint Multi-Task Learning.

Studies in health technology and informatics
Digital pathology has made significant advances in tumor diagnosis and segmentation; however, image variability resulting from tissue preparation and digitization - referred to as domain shift - remains a significant challenge. Variations caused by h...

Graph Neural Networks for Gleason Grading in Prostate Histopathology Images.

Studies in health technology and informatics
Prostate cancer is a leading cause of cancer-related deaths, with Gleason grading being key for assessing tumor aggressiveness. We propose a Graph Neural Network-based approach to automate Gleason grading using the Automated Gleason Grading Challenge...

MIMI-ONET: Multi-Modal image augmentation via Butterfly Optimized neural network for Huntington DiseaseDetection.

Brain research
Huntington's disease (HD) is a chronic neurodegenerative ailment that affects cognitive decline, motor impairment, and psychiatric symptoms. However, the existing HD detection methods are struggle with limited annotated datasets that restricts their ...

Deep learning for cerebral vascular occlusion segmentation: A novel ConvNeXtV2 and GRN-integrated U-Net framework for diffusion-weighted imaging.

Neuroscience
Cerebral vascular occlusion is a serious condition that can lead to stroke and permanent neurological damage due to insufficient oxygen and nutrients reaching brain tissue. Early diagnosis and accurate segmentation are critical for effective treatmen...

An automated cascade framework for glioma prognosis via segmentation, multi-feature fusion and classification techniques.

Biomedical physics & engineering express
Glioma is one of the most lethal types of brain tumors, accounting for approximately 33% of all diagnosed brain tumor cases. Accurate segmentation and classification are crucial for precise glioma characterization, emphasizing early detection of mali...