AI Techniques For Image Recognition

If you're involved in the development lifecycle of your companies products - read this. Teams across the product lifecycle have spent years building systems that depend on predictable customer behaviour and reliable evidence when resolving disputes. The introduction of accessible image-manipulation tools has removed the stability that many refund and quality-assurance processes rely on. The example circulating today is a manipulated burger photo that turns a cooked patty into what appears to be raw meat. Tools of this type can now produce convincing alterations in seconds. This shift affects several functions simultaneously. Customer service loses the ability to trust photo evidence. Fraud teams face a new attack vector that blends digital forgery with legitimate order data. Product managers responsible for returns, refunds, and satisfaction guarantees now operate in an environment where the traditional verification method no longer provides assurance. Teams need to respond with structured, cross functional measures: 1. Re evaluate evidence standards Photo based confirmation should not be treated as a single source of truth. Introduce multi factor validation for high risk claims. This can include structured metadata checks, behavioral risk scoring, and pattern recognition across claims. 2. Introduce tamper detection capabilities Modern image forensic models can detect common manipulation signatures. They do not eliminate the threat, but they raise the barrier and create cost for attackers. 3. Harden refund policy logic Policies relying on unconditional visual proof should transition to controlled rulesets that include order history, claim frequency, and anomaly signals. This reduces reliance on a single point of failure. 4. Educate frontline teams Operators handling disputes must understand that AI manipulation is a routine threat. Provide clear escalation paths and ensure frontline actions are consistent with enterprise risk appetite. Close the loop with product design and supply chain. Some categories can integrate unique identifiers or packaging elements that are difficult to forge. Small design choices can materially raise the cost of manipulation. AI acceleration creates opportunity, but it also creates instability in trust based systems. Product teams that absorb this early will prevent losses and maintain customer trust without compromising operational agility. This is now a core component of modern product lifecycle security, not a peripheral concern.

Most OCR fails silently. And it breaks your GenAI downstream. 𝗢𝗖𝗥 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗿𝗮𝗿𝗲𝗹𝘆 𝗳𝗮𝗶𝗹 𝗮𝘁 𝗰𝗵𝗮𝗿𝗮𝗰𝘁𝗲𝗿 𝗿𝗲𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝗼𝗻. They fail at everything after that. • layout inconsistency • missing confidence per field • model-specific output quirks • no exception fallback • fragile pre/postprocessing Most teams stop at “we extracted the text.” But real-world pipelines need to do more. 𝗛𝗼𝘄 𝗺𝗮𝘁𝘂𝗿𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝗮𝗿𝗲 𝗯𝘂𝗶𝗹𝘁: 1️⃣ 𝗜𝗻𝗽𝘂𝘁 𝗵𝗮𝗻𝗱𝗹𝗶𝗻𝗴 → Normalize DPI, grayscale, orientation using pdf2image + opencv. → Standardize all formats early to avoid layout drift later. → Minimal cleanup: denoise, keep lines intact, no need for aggressive cropping. 2️⃣ 𝗟𝗮𝘆𝗼𝘂𝘁 𝗱𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 → Use LayoutLMv3 or Detectron2 to map headers, tables, form fields. → Or go simple: group boxes by vertical/horizontal alignment to reconstruct structure. → Apply IOU-based merging or clustering to remove overlaps from stacked model predictions. 3️⃣ 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝘀𝘁𝗮𝗰𝗸 → Use Donut or docTR for full-page context (great for invoices, contracts, PDFs). → TrOCR is perfect when working with templates and predefined crops, e.g., totals, IDs, or dates. → EasyOCR for lightweight, multi-language cases. → Combine with regex fallback for high-trust fields. 4️⃣ 𝗖𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲 𝗹𝗼𝗴𝗶𝗰 → If no confidence is returned, proxy it: • 𝘛𝘰𝘬𝘦𝘯 𝘮𝘢𝘵𝘤𝘩 𝘳𝘢𝘵𝘪𝘰: compare extracted tokens to expected format or vocabulary. Low overlap = low trust. • 𝘋𝘪𝘴𝘵𝘢𝘯𝘤𝘦 𝘵𝘰 𝘬𝘯𝘰𝘸𝘯 𝘷𝘢𝘭𝘶𝘦: Levenshtein or fuzzy match against historical values (e.g., supplier names, countries). • 𝘔𝘰𝘥𝘦𝘭 𝘢𝘨𝘳𝘦𝘦𝘮𝘦𝘯𝘵 𝘳𝘢𝘵𝘦: run multiple OCR tools and compare overlaps. Divergence signals low confidence. 5️⃣ 𝗙𝗮𝗹𝗹𝗯𝗮𝗰𝗸 & 𝗿𝗼𝘂𝘁𝗶𝗻𝗴 → Chain models: 𝘧𝘢𝘴𝘵 𝘖𝘊𝘙 (e.g., EasyOCR, PaddleOCR) by default → 𝘴𝘮𝘢𝘳𝘵 𝘖𝘊𝘙 (e.g., Donut, TrOCR, custom seq2seq) on failure. → Fast OCR is layout-aware and performant for bulk docs. Smart OCR handles tricky zones, handwriting, or inconsistent layouts. → Route 𝘧𝘪𝘦𝘭𝘥𝘴, not full pages. → Track model source for auditing + drift detection. 6️⃣ 𝗘𝘅𝗰𝗲𝗽𝘁𝗶𝗼𝗻 𝗵𝗮𝗻𝗱𝗹𝗶𝗻𝗴 → Queue failures into dashboards, not logs. → Review at field level. Train on recurring patterns. → Always log raw boxes + confidences. You'll need them later. 7️⃣ 𝗢𝘂𝘁𝗽𝘂𝘁 𝗻𝗼𝗿𝗺𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 → Force every tool into one schema: text, bbox, confidence, field_name, source_model 8️⃣ 𝗦𝗮𝗻𝗶𝘁𝘆 𝗽𝗮𝘀𝘀 → Run a quick language model pass over outputs, catch junk early, flag multilingual or unexpected text. Stop worrying only about your retrieval chain. If your extraction layer is sloppy, your GenAI pipeline won’t have a chance. 💬 Which step in your pipeline causes the most OCR pain? ♻️ Repost to help someone in your network

I didn’t expect a photo of cracked eggs to spark a serious discussion about AI integrity. Yet here we are. A customer on Swiggy Instamart received a tray where only one egg was cracked. Instead of reporting it, they opened Google Gemini Nano, typed “apply more cracks,” and turned that single crack into a tray full of damaged eggs. The user sent the edited picture to support and got a full refund of ₹245. For years, support workflows were built on a simple rule: photo = proof. But that assumption doesn’t hold anymore. AI is helping businesses work smarter, automate faster, and simplify complex decisions. At the same time, misuse is becoming just as easy. A simple prompt can create fake evidence that looks more real than reality. In banking, we already deal with synthetic identities, manipulated documents, and deepfake attempts during KYC. Every time the system upgrades, fraud finds its next door. Quick commerce is now entering that same territory. If we want to stay ahead, we need systems that rethink trust: - Image authenticity checks instead of relying only on what we see - Metadata validation to catch edited or AI-generated media - Behaviour-based refund scoring models - AI models that flag anomalies instead of blindly accepting images. These aren’t big ideas. They are practical steps most companies will need very soon. AI isn’t the villain here. It’s the reminder that our systems need to mature at the same speed as the tools people now have in their pockets. How are you preparing your systems for a world where photos can’t always be trusted? #startups #AI #AITools #generativeAI #fraud RedPluto Analytics Private Limited , Kishan Sanghani

Your AI reconstruction made the scan prettier. Now prove it didn't erase pathology. FDA authors (Delfino et al.) warn that AI reconstruction/enhancement can boost perceived image quality while reducing the patient-specific information you need for diagnosis - and common image-quality metrics may not catch the failure. What to watch: 👉 "Image quality" is 3 different things (task-based, reader scores, image metrics) - and they can disagree. 👉 Optimizing SSIM/MSE trains appearance, not clinical utility. → Prospective Radiology study: reduced-dose DL reconstruction improved subjective quality, but detection of ≤0.5 cm liver lesions fell 96.9% 👉 72.3% and lesion accuracy 80.1% → 67.1%. → Deep-learning reconstruction can be unstable: tiny perturbations can add artifacts or hide structure. So what: don't go-live AI reconstruction on "looks better". Require task-based evidence for your key questions - plus a plan to detect hallucinations/artifacts over time. What's your go-live gate: lesion detectability, reader study, or continuous audit? Sources in first comment 👇 #Radiology #MedicalAI #FDA #ImageReconstruction #DeepLearning #HealthTech

The hidden moat in AI isn’t the model — it’s Label Ops. Most teams still treat data annotation like a task queue. The best teams run it like a product: specs, SLAs, and feedback loops. Here’s the 5-step QA stack we use to boost quality and cut review time: 1) Prevent > Detect - Tight ontology + examples for every edge case - Form validators in the labeling UI (required fields, regex, ranges) - "Golden hints" shown only when a rater hesitates 2) Calibrate with Gold - Small % gold labels mixed into every batch (especially if you made a change to the specs/instructions) - Track Gold Pass Rate and re-train raters who fall below threshold - Rotate golds monthly to avoid memorization 3) Measure Disagreement, not just Accuracy - Inter-Annotator Agreement (IAA) by class - Disagreement Mining: sample where raters diverge most; fix spec or add examples - Stratify audits by class rarity, not just random sampling 4) LLM-as-Judge (with guardrails) - Judge reads image/text + rubric, returns score + rationale - Debiasing: shuffle candidate order, length-normalize, mask source hints - Calibration: weekly check against a 200-item human panel; update thresholds - Only escalate low-confidence items to humans → 30–60% fewer reviews 5) Close the Loop - Turn frequent errors into new golds + UI checks - Ship a “What changed this week?” drift report (class balance, IAA, gold pass rate) - Treat annotators as partners: share examples of correct + borderline + wrong Ops Metrics that actually move the needle: - IAA ≥ 0.80 on critical classes - Golden Pass Rate trend (↑ steady = spec clarity) - Cost per 1k labels and Review Minutes per 100 labels (target ↓) - Escalation Rate from LLM-judge (falling with steady quality = win) - Drift: week-over-week deltas in label distribution & judge calibration If your model is “stuck,” don’t just add data—fix the flow that creates it. #DataOps #DataAnnotation #DataQuality #MLOps #LLMOps #HumanInTheLoop #AIEngineering #Labelbox

🚀 Introducing ImagenWorld — Making Model Failures Visible and Explainable We’ve all admired the stunning results shared from the latest image-generation models — but rarely do we see the ones that miss the mark. ImagenWorld, developed by TIGER-AI Lab, turns the spotlight on those hidden failures. It systematically uncovers how and why models stumble, offering a clearer view into the gaps behind the amazing outputs. Instead of a single opaque metric, ImagenWorld breaks model evaluation into interpretable dimensions — so we can seewhat went wrong. Each image is rated by human annotators on four criteria: 1️⃣ Prompt Relevance – Does the image follow the instruction? 2️⃣ Aesthetic Quality – Is it visually coherent and appealing? 3️⃣ Content Coherence – Do all elements logically fit together? 4️⃣ Artifacts – Are there distortions, glitches, or unreadable text? Annotators also tag specific objects and regions that caused the error using segmentation maps and VLM-assisted extraction — turning model failures into visual evidence. ImagenWorld spans major tasks across six visual domains, combining generation and editing into a unified testing ground 👉 Tasks: ▪️ TIG – Text-to-Image Generation ▪️ SRIG / MRIG – Single & Multi-Reference Image Generation ▪️ TIE – Text-Guided Image Editing ▪️ SRIE / MRIE – Single & Multi-Reference Image Editing 👉 Domains: Artworks • Photorealistic • Computer Graphics • Screenshots • Information Graphics • Textual Graphics Every one of the 20,000+ examples includes: ▪️The prompt or reference image(s) ▪️Outputs from 14 leading models (open & closed source) ▪️Human annotations pinpointing the failure cause ▪️Optional segmentation masks showing where the issue occurred ⚠️ Common Failure Modes Misinterpreting multi-step instructions Failing arithmetic or logical consistency Mislabeling or misplacing segments Regenerating entire images during edits Breaking visual logic in diagrams or charts Producing unreadable or garbled text 🌍 Why It Matters ▪️A model that “looks good” isn’t always right. ▪️A perfect composition can still fail the simple request: “Add a cup on the table.” - Instead, it places it under the table or morphs it into a vase. ImagenWorld moves beyond subjective “pretty” scores — making evaluation human-interpretable, transparent, and reproducible. It’s a bridge between creativity and accountability in AI image generation. 📊 The Scale of ImagenWorld 6 Tasks (generation + editing) 6 Visual Domains 3.6K Condition Sets 20K Human Annotations 14 Evaluated Models This is not just a dataset — it’s a framework for evaluating and improving the next generation of visual AI systems. #AI #GenerativeAI #Benchmarking #ImagenWorld #DiffusionModels #AutoregressiveAI #Research #MachineLearning #comfyui

Three, two, one—lift off! QA/QC and regulatory systems for your AI-powered lab. I’m not an AI expert. But it’s coming fast to our pathology lab. Traditional QA is simple: compare results with controls to spot what went wrong and where. When the diagnostician is an algorithm, new failure modes appear—and new controls are needed. Here’s what I learned from #PathVisions25 about AI diagnostics: ⸻ 1. Human-in-the-Loop (HITL) AI pathology isn’t about removing the pathologist—it’s about redefining where we add value. Pathologists verify tissue quality, stain adequacy, and AI overlays before the model runs. When done right, HITL becomes the best layer of QC. ⸻ 2. Good Machine Learning Practice (GMLP) FDA now expects ongoing oversight, not one-time validation. GMLP sets the standard for safe, reliable AI: data transparency, monitoring, and version control. If your vendor can’t show GMLP alignment, you’re piloting an experiment, not a diagnostic tool. ⸻ 3. Predetermined Change Control Plans (PCCP) Diagnostic systems evolve. PCCP defines what can change—thresholds, components, datasets—and how it’s tracked. Labs must know when an “update” means a new validation is required. If that boundary isn’t clear, your diagnostic is outdated. ⸻ 4. Out-of-Distribution (OOD) Detection Algorithms don’t know when they’re wrong. OOD catches inputs outside the model’s training domain—new scanners, stains, or other problems. Without it, you’re one artifact away from a high-confidence error. ⸻ 5. Posterior Class Probabilities and Drift Every prediction carries a confidence score. When those distributions shift—scanner drift, staining changes, population mix—it’s time to recalibrate. Confidence drift is the silent failure mode of AI. ⸻ 6. Sentinel Datasets Think of these as digital control slides: a fixed, well-characterized set that never changes. Re-run them periodically to confirm the scanner and algorithm behave the same. If outputs drift, something in your workflow has. ⸻ Practical checks: GMLP → local validation, post-deployment monitoring PCCP → in plan changes= OK; major changes = revalidate HITL → pathologist override rate OOD → artifacts, shifts in color/texture stats Posterior drift → spike in model abstentions Sentinel set → accuracy degradation ⸻ Bottom line: AI diagnostics aren’t static devices—they’re systems afloat in a sea of data. Keeping them safe means anchoring them with continuous monitoring, not one-time validation. The future of QA in pathology is about watching the algorithms that are watching the slides. What else should we be monitoring ? Leave a comment. Big thanks to Nicholas Spies, MD and Chris Garcia for giving a masterclass on AI QA/QC, and to Mark Lloyd for sharing his immense expertise on regulatory pathways. #ArtificialIntelligence #Medicine #Pathology #Diagnostics

Don't just use #AI-histopath without QC: "Histopathological staining of human tissue is essential for disease diagnosis. Recent advances in virtual tissue staining technologies using artificial intelligence alleviate some of the costly and tedious steps involved in traditional histochemical staining processes, permitting multiplexed staining and tissue preservation. However, potential hallucinations and artefacts in these virtually stained tissue images pose concerns, especially for the clinical uses of these approaches." "We reported AQuA, autonomous quality and hallucination assessment tool for VS tissue images, and showcased its ability in identifying morphological hallucinations formed by generative AI-based VS models. We also highlighted AQuA’s ability at the model level by accurately detecting poor-quality VS models with types of staining failure never seen before. These demonstrate its role as a gatekeeper for AI-based VS of tissue in digital pathology. The rapid rise and wide spread use of deep learning have caused concerns regarding the reliability and quality control of neural network outputs41,42, especially in critical biomedical applications such as virtual tissue staining17 ,43–45. The generative nature of VS models not only brings up risks from new attack strategies46–48 but also casts difficulty in detecting the failure modes of these models using traditional and supervised evaluation metrics. In fact, for VS of tissue, in the deployment phase of the VS model there would be no HS available, and therefore, supervised evaluation metrics based on ground-truth images cannot be used in the VS workflow. In VS-related initial studies, labourious manual quality assessments performed by pathologists on the basis of high-level semantic features and domain expertise were critical to assure the quality of VS models; this is not practical for the deployment of a VS model, which is expected to function autonomously. Therefore, AQuA provides a much needed tool for VS quality assessment and hallucination detection without access to HS ground-truth images. Through blind testing on human kidney and lung tissue samples, AQuA achieved 99.8% accuracy and 99.8% sensitivity based on its chemistry-free, unsupervised quality assessment, outperforming common structural, supervised quality assessment metrics that used HS ground-truth images. In comparison with a group of board-certified pathologists, the classification of AQuA reached 100% agreement on negative VS images, generated by good VS models, also manifesting a superhuman performance when detecting realistic-hallucinatory VS images that would normally mislead pathologists to diagnose realistic-looking VS tissue images that never existed in real life." https://lnkd.in/e2Y398pm

In research labs, measurements are clean. In manufacturing, they're… definitely not. A new AI-based approach addresses that gap, without any model training. In manufacturing environments, data rarely resembles the tidy examples we see in papers or benchmarks. Instruments are simplified for speed, cost, and robustness, samples are messy and inconsistent, and the resulting measurements often contain noise, ambiguity, and occasional errors. AI has been proposed to clean up or refine these imperfect measurements, but the core challenge is simple: you need large, controlled data to train those models, and that data is extremely hard to get at manufacturing scale. A new preprint proposes an interesting alternative that combines domain knowledge, LLMs, and Bayesian reasoning. Applied to X-ray examination of battery electrodes (where misaligned cathode/anode boundaries can cause safety failures), the authors show how these seemingly disjoint elements can be woven into a single system that recreates missing or blurred boundaries without requiring large, curated training datasets. They do this by blending: 🔹Domain knowledge as geometric priors: explicit rules defined by experts on how the structures should appear rather than learning them from thousands of examples 🔹LLM as adaptive reasoner: using an LLM to diagnose image statistics and adjust how strictly each geometric rule should be enforced 🔹Bayesian framework as the integrator: balancing what the noisy measurements suggest with what the geometry allows, and optimizing toward the most plausible configuration The result: 92% of refined outputs were accepted by quality inspectors without manual correction, all done without building or training a new ML model. The system works across different manufacturing sites without retraining. It's a compelling example of how hybrid approaches can succeed where purely data-driven models struggle. The use of an LLM as the "adaptive judge" that bridges numerical measurements and domain knowledge is both clever and pragmatic. Where else could this pattern apply? 📄 A Bayesian Method Incorporating Geometric Prior and LLM-Guided Hyperprior for Robust X-ray Electrode Mask Refinement in Lithium-ion Battery Overhang Analysis, SSRN, December 3, 2025 🔗 https://lnkd.in/eHqHDDFy

On the road to #RSNA2025, we wanted to share recently released, open-source CDRH/FDA #regulatoryscience tools to help with medical imaging software development Image Quality & Safety: • sFRC: Detect when AI "hallucinates" fake structures in medical images - critical for ensuring your AI reconstruction algorithms don't add or remove anatomical features that aren't really there [Source: https://hubs.li/Q03TxF2v0 • M-SYNTH: Test mammography AI performance across 45,000 synthetic breast images with varying densities, lesion sizes, and radiation doses - perfect for pre-submission testing without needing large patient datasets [Source: https://hubs.li/Q03TxDxk0 Digital Pathology: • SegVal-WSI: Validate how accurately your AI segments tumors vs normal tissue in gigapixel pathology slides - essential for cancer detection algorithms: [Source: https://hubs.li/Q03TxFRL0 • ValidPath: Convert whole slide images to ML-ready patches, then map AI predictions back to the original slide for pathologist review in ImageScope [Source: https://hubs.li/Q03TxCbQ0 Fairness & Generalization: • https://hubs.li/Q03TxFVD0: Compare different bias mitigation strategies to ensure your AI performs equitably across patient demographics - includes visualization tools for FDA submissions [Source: https://hubs.li/Q03TxG2K0 • DRAGen: Test if your AI will fail on new patient populations by analyzing decision space composition - catch generalization issues before clinical deployment [Source: https://hubs.li/Q03TxF520 • DomID: Discover hidden patient subgroups in your data that your AI might be treating differently - uses deep clustering to find unannotated patterns [Source: https://hubs.li/Q03TxDzz0 Clinical Impact: • QuCAD: Simulate how much time your triage AI actually saves in real emergency departments - quantify clinical benefit for FDA submissions [Source: https://hubs.li/Q03TxGLF0 • VICTRE: Run complete virtual clinical trials for mammography/DBT devices including dose calculations - test device changes without patient exposure [Source: https://hubs.li/Q03TxFHt0 Learn how to use these tools to implement best practices here: https://hubs.li/Q03TxGMz0 📚 Full catalog here: https://hubs.li/Q03TxCJk0 These tools help demonstrate safety, effectiveness, and clinical benefit for FDA submissions - all open-source and peer-reviewed. #FDA #medtech #digitalhealth #AIregulation #healthtech #510k #DeNovo #regulatoryscience