Evidence Field: Adaptive Covering Programs for Medical Imaging
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. Its encoder combines absolute and image-relative response fields with associative spatial moments and learned characteristic sums. A typed classifier scores individual regions and parent-conditioned multiplicative groups; a covering recursion selects scale, partition and composition. We establish exact one-fold coverage, optimality within the registered grammar, a bound relating smooth training to har...
| Authors | Isarar Siddique, Raiyan Siddique, Arshi Niyaz et al. |
|---|---|
| Primary category | eess.IV |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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