Fig. 1
From: Parallel multiple instance learning for extremely large histopathology image analysis
Parallel Multiple Instance Learning (P-MIL) on High-Performance-Computing (HPC) cluster. Red: positive instances; Green: negative instances. At first, we divide and distribute data to the nodes. The master will collect the results calculated by individual nodes, train multiple classifiers and choose the best one. Next, the slaves receive the best weak classifier and calculate an individual α value. The master node then will synchronize all the nodes, choose the α best and broadcast it. At last, all the nodes will update classifiers with the α best and update new clusters with the new classifiers through communication, in which the master will coordinate to ensure data coherence. The program will continue running in a loop until the loop ends