Machine Learning

Machine learning (ML) is a key area of artificial intelligence (AI) that helps computers learn from data and get better at tasks over time, without needing to be directly programmed. By recognizing patterns in data, ML algorithms can make predictions and decisions that are useful in many fields, from healthcare to finance and e-commerce. Whether it’s improving customer service or helping businesses make smarter decisions, machine learning is changing the way we interact with technology. Keep up with the latest in machine learning by following our blog for updates and insights.

Med-CTX model architecture for explainable breast cancer ultrasound segmentation using clinical reports and BI-RADS integration

Med-CTX: Revolutionizing Breast Cancer Ultrasound Segmentation with Multimodal Transformers

1 Comment / Machine Learning /

Breast cancer remains one of the most prevalent cancers worldwide, with early and accurate diagnosis being crucial for effective treatment. Medical imaging, particularly ultrasound, plays a vital role in lesion detection and characterization. However, despite advances in artificial intelligence (AI), many deep learning models used for breast cancer ultrasound segmentation still function as “black boxes,” […]

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CaLID model for 3D Volume Reconstruction

Revolutionizing Cardiac MRI with Latent Interpolation Diffusion Models for Accurate 3D Volume Reconstruction

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Introduction: The Challenge of Sparse Cardiac MRI Data Cardiac Magnetic Resonance (CMR) imaging has become an indispensable tool in modern cardiology, providing clinicians with detailed anatomical and functional information about the heart. However, a significant limitation persists in clinical practice: the acquisition of only sparse 2D short-axis slices with substantial inter-slice gaps (typically 8-10mm) rather than complete

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SCRNet: A breakthrough in medical ultrasound image segmentation

SCRNet: Spatial-Channel Regulation Network for Medical Ultrasound Image Segmentation

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Medical ultrasound imaging is a cornerstone of modern diagnostics, offering real-time, non-invasive visualization of internal organs and pathologies such as breast and thyroid nodules. However, accurate medical ultrasound image segmentation remains a significant challenge due to low contrast, speckle noise, and blurred boundaries. Traditional deep learning models often struggle to balance local contextual details and

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GeoSAM2 architecture diagram showing multi-view processing with SAM2 and LoRA modules.

GeoSAM2 3D Part Segmentation — Prompt-Controllable, Geometry-Aware Masks for Precision 3D Editing

4 Comments / Machine Learning /

In the rapidly evolving field of computer vision and 3D modeling, 3D part segmentation has emerged as a critical yet challenging task. Whether for robotic manipulation, 3D content generation, or interactive editing, accurately segmenting 3D objects into their constituent parts is essential. However, traditional methods often rely on extensive manual labeling, slow per-shape optimization, or lack fine-grained

GeoSAM2 3D Part Segmentation — Prompt-Controllable, Geometry-Aware Masks for Precision 3D Editing Read More »

A medical AI system using YOLOv8 and hyperparameter optimization to detect coronary artery stenosis in invasive coronary angiography images.

Hyperparameter Optimization of YOLO Models for Invasive Coronary Angiography Lesion Detection

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Revolutionizing Cardiac Care: How Hyperparameter Optimization Boosts YOLO Accuracy in Coronary Lesion Detection Cardiovascular diseases remain the leading cause of death worldwide, with coronary artery disease (CAD) at the forefront. Early and accurate detection of coronary stenosis—narrowing of the arteries supplying the heart—is critical for timely intervention and improved patient outcomes. While invasive coronary angiography

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Diagram illustrating the FRIES framework for estimating inconsistency in saliency metrics across deep learning models and perturbations.

FRIES: A Groundbreaking Framework for Inconsistency Estimation of Saliency Metrics

1 Comment / Machine Learning /

Unlocking Trust in AI: Introducing FRIES – The First Framework for Inconsistency Estimation of Saliency Metrics As artificial intelligence (AI) becomes increasingly embedded in high-stakes domains like healthcare, finance, and autonomous systems, the need for explainable AI (XAI) has never been greater. One of the most widely used tools in XAI is the saliency map,

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Discover RETTA: the first retrieval-enhanced test-time adaptation framework for zero-shot video captioning.

RETTA: Retrieval-Enhanced Test-Time Adaptation for Zero-Shot Video Captioning

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RETTA: Revolutionizing Zero-Shot Video Captioning with Retrieval-Enhanced Test-Time Adaptation In the rapidly evolving field of vision-language modeling, the ability to automatically generate accurate and contextually relevant descriptions of video content—known as video captioning—has become a cornerstone for applications ranging from assistive technology for the visually impaired to intelligent video search engines. While supervised models have

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Discover DeepSPV—the first deep learning pipeline to estimate 3D spleen volume from 2D ultrasound

DeepSPV: Revolutionizing 3D Spleen Volume Estimation from 2D Ultrasound with AI

7 Comments / Machine Learning /

In the rapidly evolving field of medical imaging, accurate and non-invasive assessment of organ size is critical—especially when managing chronic conditions like sickle cell disease (SCD) and liver disorders, where splenomegaly (enlarged spleen) is a common clinical indicator. Traditionally, clinicians rely on manual measurements from 2D ultrasound (US) images, which are quick and accessible but

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Visual comparison of feature clustering with CE vs. SuperCM using t-SNE plots on CIFAR-10, SVHN, and MNIST datasets—showing tighter, more separated clusters with SuperCM."

7 Shocking Ways SuperCM Boosts Accuracy (And 1 Fatal Flaw You Must Avoid)

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In the world of machine learning, semi-supervised learning (SSL) and unsupervised domain adaptation (UDA) are game-changers—especially when labeled data is scarce or expensive to obtain. But what if you could supercharge these models with a simple yet powerful technique? Enter SuperCM, a novel framework introduced in a groundbreaking 2025 Pattern Recognition paper that’s turning heads

7 Shocking Ways SuperCM Boosts Accuracy (And 1 Fatal Flaw You Must Avoid) Read More »

Infographic showing Gauging-β algorithm workflow: border detection, hierarchical clustering, and reassignment of points for superior data separation.

7 Revolutionary Clustering Breakthroughs: Why Gauging-β Outperforms (And When It Fails)

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In the rapidly evolving world of machine learning and data science, clustering algorithms are the backbone of unsupervised learning. Yet, despite decades of research, many algorithms still struggle with non-convex shapes, overlapping clusters, and sensitivity to parameters. Enter Gauging-β — a powerful new algorithm that redefines how we approach data clustering by intelligently identifying and

7 Revolutionary Clustering Breakthroughs: Why Gauging-β Outperforms (And When It Fails) Read More »

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