Image Recognition Quality Assurance In AI

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

𝗜𝗺𝗮𝗴𝗲 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗮𝗹𝗹 𝗳𝘂𝗻 𝗮𝗻𝗱 𝗴𝗮𝗺𝗲𝘀 𝘂𝗻𝘁𝗶𝗹 𝘀𝗼𝗺𝗲𝗼𝗻𝗲 𝗰𝗼𝗺𝗺𝗶𝘁𝘀 𝗳𝗿𝗮𝘂𝗱 The biggest thing that happened in AI last week was OpenAI releasing their new state-of-the-art image generator in ChatGPT, which went viral and flooded social media with cute Studio Ghibli-styled images. But if you work in financial crime compliance, this is the stuff of nightmares! For example, check out the image below where ChatGPT was used to create a synthetic ID in a single prompt. While this quick example won't get past today's ID verification solutions, a more finely tuned version probably will. This threat isn't limited to government IDs either. Any document used for KYC/KYB verification can now be forged in a similar way - e.g. incorporation documents, EIN letters, proof of address docs, bank statements. Here's how you can better protect against synthetic/forged documents: 1️⃣ 𝗚𝗼 𝗯𝗲𝘆𝗼𝗻𝗱 𝗢𝗖𝗥: Traditional document verification that only extracts text misses visual anomalies. Modern fraudsters can ensure the text is correct while tampering with visual elements. We use a combination of OCR, machine learning and multimodal models to analyze documents. 2️⃣ 𝗠𝗲𝘁𝗮𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗶𝘀 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹: Every digital document leaves traces of its creation and modification history. At Parcha, we analyze document metadata to detect tampering attempts—examining everything from creation timestamps to digital signatures. These digital fingerprints reveal subtle traces that even sophisticated fraudsters can't completely erase. 3️⃣ 𝗠𝘂𝗹𝘁𝗶-𝗹𝗮𝘆𝗲𝗿𝗲𝗱 𝘃𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Deploy solutions that combine visual analysis, metadata inspection, and content validation. Each layer adds a barrier that fraudsters must overcome, exponentially increasing the difficulty of successful fraud. 4️⃣ 𝗣𝗶𝘅𝗲𝗹-𝗹𝗲𝘃𝗲𝗹 𝘀𝗰𝗿𝘂𝘁𝗶𝗻𝘆: The most advanced forgeries often show inconsistencies at the microscopic level. We've built systems that examine documents at the pixel level—analyzing font consistency, color patterns, and even subtle variations in image compression. As generative AI becomes more accessible, we'll see an arms race between fraudsters and compliance teams. The best prepared compliance teams will be those who leverage AI not just to detect obvious forgeries but to spot the subtle inconsistencies that even the most sophisticated AI-generated documents can't hide. The good news? The same technology powering this generation wave is also enabling more sophisticated detection. That's why we've focused on building multi-modal AI agents that examines documents the way human experts do—catching the subtle irregularities in seals, signatures, and formatting that traditional systems miss. Check out the link in comments to learn more!

New research from University of Illinois Urbana-Champaign and SyBridge Technologies shows an EfficientNetV2 machine vision model can identify 3D printed part sources with 98.5% accuracy from high-resolution images alone - no labels or tags or supplier collaboration required. Critical for safety-critical industries - this AI model can be directly inserted into quality assurance processes while improving materials intake throughput: ✈️ Aerospace - combat counterfeit parts ☢️ Nuclear - prevent component fraud 💻 Electronics - stop IC counterfeiting and early failures The AI detects "manufacturing fingerprints" invisible to humans, works retroactively on existing parts, and can't be tampered with like traditional tracking methods. Perfect for supply chains where counterfeit parts aren't just costly - they're catastrophic. #Manufacturing #AI #SupplyChain #AdditiveManufacturing #QualityAssurance