AIMC Topic: Image Interpretation, Computer-Assisted

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Application of cloud server-based machine learning for assisting pathological structure recognition in IgA nephropathy.

Journal of clinical pathology
BACKGROUND: Machine learning (ML) models can help assisting diagnosis by rapidly localising and classifying regions of interest (ROIs) within whole slide images (WSIs). Effective ML models for clinical decision support require a substantial dataset o...

Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence-Assisted Cancer Diagnosis.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
The potential of artificial intelligence (AI) in digital pathology is limited by technical inconsistencies in the production of whole slide images (WSIs). This causes degraded AI performance and poses a challenge for widespread clinical application, ...

ResViT FusionNet Model: An explainable AI-driven approach for automated grading of diabetic retinopathy in retinal images.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: Diabetic Retinopathy (DR) is a serious diabetes complication that can cause blindness if not diagnosed in its early stages. Manual diagnosis by ophthalmologists is labor-intensive and time-consuming, particularly in overburd...

Adapting to evolving MRI data: A transfer learning approach for Alzheimer's disease prediction.

NeuroImage
Integrating 3D magnetic resonance imaging (MRI) with machine learning has shown promising results in healthcare, especially in detecting Alzheimer's Disease (AD). However, changes in MRI technologies and acquisition protocols often yield limited data...

Predicting Paediatric Brain Disorders from MRI Images Using Advanced Deep Learning Techniques.

Neuroinformatics
The problem at hand is the significant global health challenge posed by children's diseases, where timely and accurate diagnosis is crucial for effective treatment and management. Conventional diagnosis techniques are typical, use tedious processes a...

Investigating the key principles in two-step heterogeneous transfer learning for early laryngeal cancer identification.

Scientific reports
Data scarcity in medical images makes transfer learning a common approach in computer-aided diagnosis. Some disease classification tasks can rely on large homogeneous public datasets to train the transferred model, while others cannot, i.e., endoscop...

UnICLAM: Contrastive representation learning with adversarial masking for unified and interpretable Medical Vision Question Answering.

Medical image analysis
Medical Visual Question Answering aims to assist doctors in decision-making when answering clinical questions regarding radiology images. Nevertheless, current models learn cross-modal representations through residing vision and text encoders in dual...

Dynamic spectrum-driven hierarchical learning network for polyp segmentation.

Medical image analysis
Accurate automatic polyp segmentation in colonoscopy is crucial for the prompt prevention of colorectal cancer. However, the heterogeneous nature of polyps and differences in lighting and visibility conditions present significant challenges in achiev...

When multiple instance learning meets foundation models: Advancing histological whole slide image analysis.

Medical image analysis
Deep multiple instance learning (MIL) pipelines are the mainstream weakly supervised learning methodologies for whole slide image (WSI) classification. However, it remains unclear how these widely used approaches compare to each other, given the rece...

Assessment of different U-Net backbones in segmenting colorectal adenocarcinoma from H&E histopathology.

Pathology, research and practice
Adenocarcinoma, the most prevalent type of colorectal cancer, makes up roughly 95 % of all cases and is associated with a notably high mortality rate. Owing to the various risk factors which might include personal choices and habits or genetic factor...