Evidence Field: Adaptive Covering Programs for Medical Imaging

Key points

• Evidence Field is a trainable spatial-program architecture that jointly produces class scores, selected covering programs and signed evidence.1
• Its encoder combines absolute and image-relative response fields with associative spatial moments and learned characteristic sums.1
• A typed classifier scores individual regions and parent-conditioned multiplicative groups; a covering recursion selects scale, partition and composition.1
• We establish exact one-fold coverage, optimality within the registered grammar, a bound relating smooth training to hard inference, signed-score conservation and class-centred offset invariance.1

Sources (1)

• [1]Evidence Field: Adaptive Covering Programs for Medical Imaging
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 04:31 PM

  Medical image classifiers typically return predictions without a native contract for independently reconstructing their spatial decision computation.

  Evidence Field is a trainable spatial-program architecture that jointly produces class scores, selected covering programs and signed evidence.

Extractive summary: sentences quoted from the sources.