Adnan Saeed

Adnan Saeed is a deep learning researcher working on medical image analysis, with a focus on multimodal architectures, graph neural networks, and evidential deep learning for clinical imaging tasks. His peer reviewed research has appeared in journals across machine learning and biomedical signal processing. At AI Trend Blend he turns recent papers into clear, practical explainers, with an emphasis on what a method actually does and where it holds up, written for readers who want depth without the hype.

M2OTCA: How Multi-Magnification Optimal Transport Finally Made Whole Slide Image AI Work the Way Pathologists Think.

M2OTCA: How Multi-Magnification Optimal Transport Finally Made Whole Slide Image AI Work the Way Pathologists Think

M2OTCA: How Multi-Magnification Optimal Transport Finally Made Whole Slide Image AI Work the Way Pathologists Think | AI Trend Blend AITrendBlend Machine Learning Medical AI Computer Vision Image Segmentation About Medical AI · Medical Image Analysis 112 (2026) 104082 · 18 min read M2OTCA Taught AI to Read Cancer Slides the Way a Pathologist Does […]

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CardioMorphNet: Shape-Guided Bayesian Recurrent Deep Learning for 3D Cardiac Motion Estimation.

CardioMorphNet: Shape-Guided Bayesian Recurrent Deep Learning for 3D Cardiac Motion Estimation

CardioMorphNet: Shape-Guided Bayesian Recurrent Deep Learning for 3D Cardiac Motion Estimation | AI Trend Blend AITrendBlend Machine Learning Cybersecurity About Medical AI · Medical Image Analysis 113 (2026) 104149 · 18 min read CardioMorphNet Taught an AI to Track Your Heartbeat Without Ever Looking at Raw Pixels Researchers at the University of Glasgow and the

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When Enzymes Hit Their Limits — A Rigorous Look at the Total Quasi-Steady State Approximation.

When Enzymes Hit Their Limits — A Rigorous Look at the Total Quasi-Steady State Approximation

When Enzymes Hit Their Limits — A Rigorous Look at the Total Quasi-Steady State Approximation | AI Trend Blend Applied Mathematics · Journal of Mathematical Analysis and Applications 561 (2026) · Louisiana State University & University of Nottingham · 15 min read When Enzymes Hit Their Limits — A Rigorous Stochastic Proof of the Total

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Chaos in the p-adic Ising Model — When Prime Numbers Decide Phase Transitions.

Chaos in the p-adic Ising Model — When Prime Numbers Decide Phase Transitions

Chaos in the p-adic Ising Model — When Prime Numbers Decide Phase Transitions | AI Trend Blend AITrendBlend Machine Learnings Mathematics About Statistical Mechanics · Journal of Mathematical Analysis and Applications 560 (2026) · UAE University & Uzbekistan Academy of Sciences · 14 min read When the Prime Number Decides Everything — Chaos and Phase

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gsplat: An Open-Source Library for Gaussian Splatting.

gsplat: An Open-Source Library for Gaussian Splatting

gsplat: An Open-Source Library for Gaussian Splatting | Research Breakdown AITrendBlend Computer Vision Machine Learning About 3D Reconstruction gsplat: The Open-Source Library That Is Making Gaussian Splatting Faster, Leaner, and More Accessible Than Ever A team from UC Berkeley, Aalto University, ShanghaiTech, SpectacularAI, Amazon, and Luma AI built an open-source PyTorch library for Gaussian Splatting

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Sliced-Wasserstein Distances and Flows on Cartan-Hadamard Manifolds.

Sliced-Wasserstein Distances and Flows on Cartan-Hadamard Manifolds

Sliced-Wasserstein Distances and Flows on Cartan-Hadamard Manifolds | AI Trend Blend AITrendBlend Machine Learning Cybersecurity About Optimal Transport · Journal of Machine Learning Research 26 (2025) 1–76 · 18 min read Measuring Distance Between Distributions on Curved Spaces Just Got a Lot Faster Bonet, Drumetz, and Courty from ENSAE, IMT Atlantique, and Universite Bretagne Sud

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Statistical Inference via Sketched StoSQP: Online Second-Order Methods for Constrained Optimization.

Statistical Inference via Sketched StoSQP: Online Second-Order Methods for Constrained Optimization

Statistical Inference via Sketched StoSQP: Online Second-Order Methods for Constrained Optimization | AI Trend Blend Optimization Theory · Journal of Machine Learning Research 26 (2025) 1–75 · 20 min read The Online Inference Problem That Second-Order Methods Finally Solved — Without Projections Sen Na at Georgia Tech and Michael Mahoney at UC Berkeley prove that

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Building Computer Vision Pipelines with Claude Code (2026 Guide).

Building Computer Vision Pipelines with Claude Code (2026 Guide)

Building Computer Vision Pipelines with Claude Code (2026 Guide) | AITrendBlend AITrendBlend AI Agents Claude Machine Learning ChatGPT Home › Tutorials › Building Computer Vision Pipelines with Claude Code Computer Vision Claude Code Python Object Detection OpenCV YOLOv11 OCR 2026 Building Computer Vision Pipelines with Claude Code AITrendBlend Editorial | May 27, 2026 | 14

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YOLOv11 Object Detection: From Zero to Deployment (2026 Guide).

YOLOv11 Object Detection: From Zero to Deployment (2026 Guide)

YOLOv11 Object Detection: From Zero to Deployment (2026 Guide) | AITrendBlend AITrendBlend AI Agents Claude Machine Learning Gemini Home › Tutorials › YOLOv11 Object Detection: From Zero to Deployment YOLOv11 Object Detection Python Ultralytics Custom Training ONNX Export FastAPI Computer Vision YOLOv11 Object Detection: From Zero to Deployment AITrendBlend Editorial | May 27, 2026 |

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The ODE Method for Stochastic Approximation with Markovian Noise: Breaking the Deadly Triad in Reinforcement Learning.

The ODE Method for Stochastic Approximation with Markovian Noise: Breaking the Deadly Triad in Reinforcement Learning

The ODE Method for Stochastic Approximation with Markovian Noise: Breaking the Deadly Triad in Reinforcement Learning | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Reinforcement Learning Theory · Journal of Machine Learning Research 26 (2025) 1–76 · 20 min read The ODE Method Gets Its Markovian Upgrade — and Reinforcement Learning’s Most Stubborn

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