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.

Weak-Mamba-UNet: How CNN, ViT, and Visual Mamba Collaborate to Segment Medical Images from Scribbles

Weak-Mamba-UNet: How CNN, ViT, and Visual Mamba Collaborate to Segment Medical Images from Scribbles

Weak-Mamba-UNet: How CNN, ViT, and Visual Mamba Collaborate to Segment Medical Images from Scribbles | AI Trend Blend Medical AI & Weakly-Supervised Learning · arXiv:2402.10887 · University of Oxford / Mianyang Visual Engineering Center · 25 min read Teaching Three Different Brains to Agree — How Weak-Mamba-UNet Segments Hearts from Scribbles Ziyang Wang at Oxford […]

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Mamba-3: Three Simple Ideas That Finally Fix What Transformers Get Wrong at Inference.

Mamba-3: Three Simple Ideas That Finally Fix What Transformers Get Wrong at Inference

Mamba-3: Three Simple Ideas That Finally Fix What Transformers Get Wrong at Inference | AI Trend Blend AITrendBlend Machine Learning NLP & LLMs About Efficient AI · arXiv:2603.15569 · CMU & Princeton · March 2026 · 22 min read Mamba-3: Three Simple Ideas That Finally Fix What Transformers Get Wrong at Inference Time Researchers at

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Through the Perspective of LiDAR: Uncertainty-Aware Annotation Pipeline for TLS Point Cloud Segmentation.

Through the Perspective of LiDAR: Uncertainty-Aware Annotation Pipeline for TLS Point Cloud Segmentation

Through the Perspective of LiDAR: Uncertainty-Aware Annotation Pipeline for TLS Point Cloud Segmentation | AI Trend Blend AITrendBlend Machine Learning Computer Vision About 3D Vision & Forest AI · ISPRS J. Photogramm. Remote Sens. 236 (2026) 141–161 · Rochester Institute of Technology / US Forest Service · 24 min read Seeing the Forest Through LiDAR:

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FGI-EMIT: The First Multispectral LiDAR Benchmark That Finally Takes Understory Trees Seriously.

FGI-EMIT: The First Multispectral LiDAR Benchmark That Finally Takes Understory Trees Seriously

FGI-EMIT: The First Multispectral LiDAR Benchmark That Finally Takes Understory Trees Seriously | AI Trend Blend AITrendBlend Machine Learning Computer Vision About 3D Forest AI & Remote Sensing · ISPRS J. Photogramm. Remote Sens. 236 (2026) 569–605 · FGI / Aalto University · 30 min read The Forest Floor’s Hidden Trees — How a New

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CRGenNet: Cloud-Free Optical Image Generation Using SAR and Contaminated Optical Data.

CRGenNet: Cloud-Free Optical Image Generation Using SAR and Contaminated Optical Data

CRGenNet: Cloud-Free Optical Image Generation Using SAR and Contaminated Optical Data | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Remote Sensing AI · ISPRS Journal of Photogrammetry and Remote Sensing 236 (2026) 255–272 · 22 min read CRGenNet: How Satellites Can See Through Clouds by Never Assuming the Sky Is Clear Researchers at

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GateMamba: Feature Gated Mixer in State Space Model for Point Cloud 3D Object Detection.

GateMamba: Feature Gated Mixer in State Space Model for Point Cloud 3D Object Detection

GateMamba: Feature Gated Mixer in State Space Model for Point Cloud 3D Object Detection | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Autonomous Driving AI · ISPRS Journal of Photogrammetry and Remote Sensing 236 (2026) 640–653 · 22 min read GateMamba: How Three Gated Mixers Taught a Mamba Network to Stop Ignoring Cyclists

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Best Image Generation AI Tools.

Best Image Generation AI Tools Compared: Midjourney vs DALL-E 3 vs Firefly vs Stable Diffusion (2026)

Best Image Generation AI Tools Compared: Midjourney vs DALL-E 3 vs Firefly vs Stable Diffusion (2026) AItrendblend Tech News Machine Learning Prompts AI Tools / Image Generation / 2026 Comparison Best Image Generation AI Tools Compared in 2026 Midjourney v7, DALL-E 3, Adobe Firefly 3, Stable Diffusion / Flux, and Google Imagen 3 put side

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The Moon's Many Faces: A Single Unified Transformer for Multimodal Lunar Reconstruction

The Moon’s Many Faces: A Single Unified Transformer for Multimodal Lunar Reconstruction

The Moon’s Many Faces: A Single Unified Transformer for Multimodal Lunar Reconstruction | AI Trend Blend Planetary AI & 3D Reconstruction · ISPRS J. Photogramm. Remote Sens. 236 (2026) 363–379 · TU Dortmund University · 26 min read The Moon’s Many Faces: How One Transformer Learned to Speak All Four Languages of Lunar Science Simultaneously

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CS-EMCF: Compressive Sensing Phase Unwrapping for SAR Interferometry.

CS-EMCF: Compressive Sensing Phase Unwrapping for SAR Interferometry

CS-EMCF: Compressive Sensing Phase Unwrapping for SAR Interferometry | AI Trend Blend Remote Sensing AI · ISPRS Journal of Photogrammetry and Remote Sensing 236 (2026) 120–140 · 22 min read How Compressive Sensing Finally Broke the Phase Unwrapping Bottleneck in SAR Interferometry Researchers at Italy’s CNR-IREA fused decades-old minimum cost flow theory with modern compressive

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GEF: Gaussian Entropy Fields for 3D Surface Reconstruction.

GEF: Gaussian Entropy Fields for 3D Surface Reconstruction

GEF: Gaussian Entropy Fields for 3D Surface Reconstruction | AI Trend Blend AITrendBlend Machine Learning Computer Vision About 3D Computer Vision · ISPRS Journal of Photogrammetry and Remote Sensing 236 (2026) 273–285 · 24 min read GEF: What If the Secret to Better 3D Reconstruction Was Treating Surface Uncertainty as Entropy? Researchers at Shandong University

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