What's your boundary tolerance—and who's verifying it? In medical imaging, a 2mm segmentation error doesn't fail a metric. It fails a patient. 📩contact@pareidolia.in 🌐pareidolia.in We Annotate. You Innovate. #MedicalAI #ImageSegmentation #HealthcareAI #DataAnnotation

💥Excited for the publication: "PixMed-Enhancer: An Efficient Approach for Medical Image Augmentation" 🔗brnw.ch/21x2Gop 📌 #MedicalImaging #GenerativeAI #GAN #AIinHealthcare #DeepLearning #ImageSegmentation #Radiology #MachineLearning #ClinicalAI

🔬 "Biomedical Imaging and Data Analytics for Disease Diagnosis and Treatment, 3rd Edition" is back and open for submissions! 🕑 Deadline: 31 October 2026 🎉 Submit your research now! 🔗 brnw.ch/21x2CeD #QuantitativeBiomarkers #ImageSegmentation #PatternRecognition

🧠 "Diagnostic Biomedical Image and Processing with Artificial Intelligence and Deep Learning — 2nd Edition" is back and open for submissions! 🕑 Deadline: 31 August 2026 🎉 Submit your research now! 🔗 brnw.ch/21x2bYn #ImageSegmentation #ExplainableAI #DeepLearning

Is high accuracy enough? Or is the "black box" problem the biggest bottleneck for AI in critical sectors like #defense and #medical or #finance ? ​In fields like defense where the margin of error is zero, it's not enough for a deep learning model to just be "correct." It must explain how and where it looked to make that decision. In our new paper published in the Scopus-indexed El-Cezeri Journal, we tackled this exact issue. ​Placing Explainable AI (#XAI) at the absolute center of our methodology, we introduced an Attention-ResUNet architecture for detecting heavily camouflaged military targets. Here is a breakdown of our approach in the attached images: ​🔍 Transparent Decisions: By integrating "Attention Coefficients" into our ResNet-50 backbone, we mathematically proved exactly where the network focuses during prediction, effectively shattering the black box. ⚙️ Robust Pipeline: Our end-to-end reproducible workflow from raw ACD1K dataset loading to final evaluation. 📈 Proven Performance: Driven by CLAHE preprocessing and Adam optimization, we achieved a 98.32% Accuracy. We proved that transparency doesn't mean sacrificing performance. ​Explainability in AI is no longer an optional add-on; it must be the core. Huge thanks to my students for their exceptional work. ​🔗 Read the full paper: dergipark.org.tr/tr/pub/ecjs… ​#DeepLearning #ExplainableAI #XAI #ComputerVision #ObjectDetection #MachineLearning #ImageSegmentation #DefenseTech #ArtificialIntelligence

🤖 "AI Advancements in Healthcare: Medical Imaging and Sensing Technologies, 2nd Edition" is back and open for submissions! 🕑 Deadline: 31 August 2026 🎉 Submit your research now! 🔗 brnw.ch/21x2a18 #ImageSegmentation #ComputerAidedDiagnosis #BiosignalProcessing

HALVS dataset & DDLR framework enable label-efficient hierarchical leaf vein segmentation across species, advancing plant physiology and agricultural research. 🍃🔬 #PlantScience #ImageSegmentation Details: doi.org/10.1016/j.plaphe.202…

🤖 "Artificial Intelligence in Biomedical Imaging and Biomedical Signal Processing: Second Edition" is back and open for submissions! 🕑 Deadline: 31 March 2027 🎉 Submit your research now! 🔗 brnw.ch/21x1DHW #DeepLearning #ImageSegmentation #MedicalDiagnostics

💥 New Special Issue open for submission! 🔗 tinyurl.com/5n7vujty 🕑 The deadline for manuscript submissions is 31 December 2026 📌 #imageanalysis #diseaseclassification #imagesegmentation #deeplearning

💥Check out the publications from Yale University in 2024-25 🏫@Yale 📌#CardiovascularResearch #NeuralNetworks #ImageSegmentation #MedicalImaging #NeuroImaging #DeepLearning

New tutorial! 🚀 Advanced SAM 3: Multi-Modal Prompting and Interactive Segmentation 👉 pyimg.co/5c4ag 👍 Author: Piyush Thakur #SAM3 #ComputerVision #ImageSegmentation #AI #DeepLearning #PyImageSearch

💥Highly recommended publication: "Advancing Ocular Imaging: A Hybrid Attention Mechanism-Based U-Net Model for Precise Segmentation of Sub-Retinal Layers in OCT Images" 🔗 tinyurl.com/yskbnzej 🏫 @AucklandUni 📌#OCT #subretinallayers #imagesegmentation #UNet #ophthalmology

💥Highly recommended publication: "Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches" 🔗 shorturl.at/Tuv1t 🏫 @BristolUni @UniofNottingham 📌#imagesegmentation #imageprocessing #deeplearning #diagnostic

Image segmentation breaks an image into meaningful regions. This video explains the three main types: • Semantic segmentation • Instance segmentation • Panoptic segmentation Resources 📘 Semantic Segmentation (DeepLab v3 ) → vist.ly/4m4c5 Instance Segmentation (Mask R-CNN) → vist.ly/4m4c8 Panoptic Segmentation (UPSNet) → vist.ly/4m4c9 Free OpenCV Bootcamp → vist.ly/4m4c4 #ComputerVision #ImageSegmentation #SemanticSegmentation #InstanceSegmentation #PanopticSegmentation #OpenCV

New tutorial | Object isolation with Ultralytics YOLO11! 🎨 Segment objects and isolate them on black or transparent backgrounds, plus overlay masks on new images using Python. Watch here ➡️ ow.ly/Zy5R50Xu1Lw #ImageSegmentation #ComputerVision

Introducing the new #GTIApp! Learn #imagesegmentation with #MATLAB in an interactive environment: Otsu, k-means, Watershed, Active Contours, and more. Visualize results, adjust parameters, and experiment in real time. gti.ssr.upm.es/gie-alive/too… @La_UPM @telecoupm

📢 New publication! “A Crawling Review of Fruit Tree Image Segmentation” by Oh & Lee is now out. 🔗Read it in full here: mdpi.com/2077-0472/15/21/223… #Agriculture #ComputerVision #DeepLearning #SmartFarming #ImageSegmentation

🔬 Excited to share the publication "Self-Attention Diffusion Models for Zero-Shot Biomedical Image Segmentation: Unlocking New Frontiers in Medical Imaging". 👉 brnw.ch/21wWRlr #MedicalImaging #ZeroShotLearning #DiffusionModels #DeepLearning #ImageSegmentation

Googleの「Conversational image segmentation with Gemini 2.5」を試してみました。 具体的には、自作ゲーム3点のスクリーンショットを読み込ませ、様々なセグメンテーションマスクの検出処理を実行しました。 例えば、「Casual Tennis」のスクリーンショットを読み込ませ、全ての物体、観客、最も遠くに配置されている椅子、プレイヤーが打ったボールなどを検出させました。 # 結果・所感 動画は実行結果となります。 オブジェクトを囲む四角い枠は検出した物体のバウンディングボックス、"テニスボール"などのテキストは物体の名称、物体の形状に沿った領域（紫や緑などで着色）はセグメンテーションマスクの物体部分となります。 小さい観客などは一部検出できないといった課題はあるようですが、基本的には短時間で高精度に検出できる非常に優秀な検出機能だと思います。 本機能は自動運転車、医療画像解析、写真・動画編集、そしてゲーム開発など多くの分野へ応用できる可能性を秘めた、非常に将来性のある技術であり、今後のさらなる進化に期待しています。 # 動画タイムライン ### 1. Casual Tennis - **0:00-0:22:** 「全ての物体」のセグメンテーションマスクの検出結果 - **0:22-0:34:** テニスボール - **0:34-0:39:** 椅子 - **0:39-0:52:** キャラクター - **0:52-0:57:** 最も遠くに配置されている椅子 - **0:57-1:02:** プレイヤーが打ったボール - **1:02-1:08:** 観客 ### 2. Space Shooter With Adviser - **1:08-1:45:** 全ての物体 ### 3. Color Echo - **1:45-2:06:** 全ての物体 # 今後の展望 ### 1. 本機能をプログラムから利用 Google AI SDKを利用して、本機能を試すためのPythonプログラムを作成します。 ### 2. ゲーム開発への応用 ゲーム開発に応用できることを探ります。 具体的には、以下のようなことを試します。 - **デバッグ作業の効率化** ゲームのスクリーンショットを分析し、不自然な点（キャラクターが壁にめり込んでいるなど）を自動で検出させます。 - **本機能を利用した斬新なゲームの創出** LLMに自然言語で指示を出すと、ユニットを自動で検出し、命令を実行する機能を持った斬新なRTS（リアルタイムストラテジー）を開発します。 例えば、「川の向こう岸にいる、HPが減っているユニットだけを後退させてください。」と指示を出すと、自動でその条件のユニットを検出し、後退させるような機能です。 # 関連URL - [Conversational image segmentation with Gemini 2.5 | Google Developers Blog](developers.googleblog.com/en…) - [本機能の利用はこちらから](aistudio.google.com/app/apps…) - [Gemini 2.5 による会話型画像セグメンテーション ｜ npaka](note.com/npaka/n/n0a006aef06…) - [Casual Tennis | miya](x.com/miya00907380/status/19…) - [Color Echo | miya](x.com/miya00907380/status/19…) - [Space Shooter With Adviser | miya](x.com/miya00907380/status/19…) # タグ #Google #Gemini #ImageSegmentation #ObjectDetection #GameDevelopment #AI