Fully supervised Deep Learning methods are very effective in providing high performance. However, this approach has two major drawbacks. Firstly fully supervised methods require tedious expert annotations. Secondly, the AI trained on expert labels cannot surpass the standard of the expert and therefore AI performance degrades as the labels become noisy. We proposed an indirect way of training our AI models using a weakly supervised, multi-instance, multi-task learning paradigm that avoids detailed annotations. With this indirect way of training our AI, we demonstrated that our learning paradigm can elucidate instance-level features very well. This work also provides mathematical guarantees on some properties of our weakly supervised method.