Glaucoma, which can cause irreversible damage to the sight of human eyes, is conventionally diagnosed by visual field (VF) sensitivity. However, it is labor-intensive and time-consuming to measure VF. Recently, optical coherence tomography (OCT) has been adopted to measure retinal layers thickness (RT) for assisting the diagnosis because glaucoma makes structural changes to RT and it is much less costly to obtain RT. In particular, RT can assist in mainly two manners. One is to estimate a VF from an RT such that clinical doctors only need to obtain an RT of a patient and then convert it to a VF for the diagnosis. The other is to predict future VFs by utilizing both past VFs and RTs, i.e., the prediction of progression of VF over time. The two computational tasks are performed as two data mining tasks because currently there is no knowledge about the exact form of the computations involved. In this paper, we study a novel problem which is the integration of the two data mining tasks. The motivation is that both the two data mining tasks deal with transforming information from the RT domain to the VF domain such that the knowledge discovered in one task can be useful for another. The integration is non-trivial because the two tasks do not share the way of transformation. To address this issue, we design a progression-agnostic and mode-independent (PAMI) module which facilitates cross-task knowledge utilization. We empirically demonstrate that our proposed method outperforms the state-of-the-art method for the estimation by 6.33% in terms of mean of the root mean square error on a real dataset, and outperforms the state-of-the-art method for the progression prediction by 3.49% for the best case.

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