Individual cells are heterogeneous when responding to environmental cues. Under an external signal, certain cells activate gene regulatory pathways, while others completely ignore that signal. Mechanisms underlying cellular heterogeneity are often inaccessible because experiments needed to study molecular states destroy the very states that we need to examine. Here, we developed an image-based support vector machine learning model to uncover variables controlling activation of the immune pathway nuclear factor ΚB (NF-ΚB). Computer vision analysis predicts the identity of cells that will respond to cytokine stimulation and shows that activation is predetermined by minute amounts of “leaky” NF-ΚB (p65:p50) localization to the nucleus. Mechanistic modeling revealed that the ratio of NF-ΚB to inhibitor of NF-ΚB predetermines leakiness and activation probability of cells. While cells transition between molecular states, they maintain their overall probabilities for NF-ΚB activation. Our results demonstrate how computer vision can find mechanisms behind heterogeneous single-cell activation under proinflammatory stimuli.