--- title: Day 4 of 7 Days of Teaching AI to See | daily.dev description: A personal build log chronicles day four of a week-long project teaching an AI model (nicknamed 'Ver') to analyze MRI scans. The model now detects anomalies... canonical: https://daily.dev/posts/day-4-of-7-days-of-teaching-ai-to-see-wq6uieca7 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Day 4 of 7 Days of Teaching AI to See | daily.dev og:description: A personal build log chronicles day four of a week-long project teaching an AI model (nicknamed 'Ver') to analyze MRI scans. The model now detects anomalies... og:url: https://daily.dev/posts/day-4-of-7-days-of-teaching-ai-to-see-wq6uieca7 og:image: https://api.daily.dev/og/posts/WQ6uiECA7.png og:image:alt: Day 4 of 7 Days of Teaching AI to See og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Day 4 of 7 Days of Teaching AI to See **[Taz](https://daily.dev/sources/sye1wuv27hcjlpobmwwlb)** · [@lilmas_t](https://daily.dev/lilmas_t) · 1 min read · 0 upvotes · 0 comments ## Summary A personal build log chronicles day four of a week-long project teaching an AI model (nicknamed 'Ver') to analyze MRI scans. The model now detects anomalies and patterns in MRI slices rather than just training on loss values, with a pipeline moving from raw MRI data through pattern detection, segmentation, anomaly visualization, and API response. The setup remains CPU-friendly and still needs more real-data training and stronger models. ## Content Something interesting happened today. Ver started noticing things. Not in the “I have become sentient” way. We're not unplugging the servers just yet. Ver can now detect anomalies and patterns in MRI slices, turning raw pixels into something a little more meaningful. The pipeline is becoming: MRI data → Pattern detection → Segmentation → Anomaly visualization → API response And honestly, this is where the project starts feeling different. We're no longer just training a model and staring at loss values hoping for emotional support. We're asking: “What is the model actually seeing?” There are still plenty of things to improve. More real-data training, better evaluation, stronger models, and eventually moving beyond our tiny CPU-friendly setup. But today, Ver crossed a small milestone: It can look at an image and point toward something unusual. Tomorrow, we make that vision easier for a human to understand ## Similar posts on daily.dev - [Day 20: I Started This to Fix My AI Skepticism. 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