AI画像解析による乳がんの早期予測（リスク評価） AI が乳がん化を予測 実際にガン化 ⽂献： McKinney, S.M., Sieniek, M., Godbole, V. et al. International evaluation of an AI system for breast cancer screening. Nature 577, 89‒94 (2020). 画像出典： https://news.mit.edu/2019/using-ai-predict-breast-cancer-and-personalize-care-0507 マンモグラフィ検診は，⼈間でも判定が難しいので，AI⽀援は がん予測の精度向上と⼈間の作業負荷の削減につながる 8
機械学習・データマイニングの分析技術のコアは数学であるが… 㻚㻜㻜㻓㻔 㻔㻜㻜㻜㻓㻚 㻕㻓㻓㻓㻓㻔 㻕㻓㻓㻓㻓㻚 㻕㻓㻓㻔㻓㻔 㻕㻓㻓㻔㻓㻚 㻕㻓㻓㻕㻓㻔 㻕㻓㻓㻕㻓㻚 㻕㻓㻓㻖㻓㻔 㻕㻓㻓㻖㻓㻚 㻕㻓㻓㻗㻓㻔 㻕㻓㻓㻗㻓㻚 㻕㻓㻓㻘㻓㻔 㻕㻓㻓㻘㻓㻚 㻕㻓㻓㻙㻓㻔 㻕㻓㻓㻙㻓㻚 㻕㻓㻓㻚㻓㻔 㼗㼌㼐㼈 crease and decrease in the number of ncerning a fact that a common-sense fact is found on the ase in a similar manner as time passes. The and expired states are represented by a uni- ibution. In total, the temporal distribution nce of a statement on the Web is modeled as ibution. matical formulation is as follows. We repre- Recognition Model using a mixture distribu- ans the probability that web page about a be created at time t. It is expressed as a lin- n of a Gaussian distribution N(t; µ, σ2) with an exponential distribution f(t) with weight = α1N(t; µ, σ2) + α2f(t) (1) i: index for distributions (i ∈ {1, 2}). αi : weight for distribution i. λ: parameter for the exponential distribution. µ: mean vector for the Gaussian. σ2: variance for the Gaussian. φi: parameter vector (αi, λ, µ, σ2). pi(xk |φi): probability of xk by distribution i. Φ: parameter vector for the mixture model. p(xk |Φ): probability of xk by the mixture model. select initial estimated parameter vector Φ until Φ converges to Φ do Φ ← Φ for each i do initialize Ψi , Mi , Si for each k do ψik ← αipi(xk|φi) p(xk|Φ) Ψi ← Ψi + ψik Mi ← Mi + ψik xk if i = 1 then Si ← Si + ψik (xk − µ)2 αi ← Ψi n if i = 1 then µ ← Mi Ψi , σ2 ← Si Ψi if i = 2 then λ ← − Ψi Mi return Φ
This algorithm is based on the calculation in Appendix A. 数理的内容の学習は，初学者にはハードルが高い 12
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