Manifold Learning & Stacked Sparse Autoencoder for Robust Breast Cancer Classification from Histopathological Images

Computer aided diagnosis (CAD) of histopathological images (HI) requires efficient structural representation of the underlying surface tissue convolutions as manifested by the diverse breast cancerous (BC) tissue morphology. In this contribution, HI are modelled as spatially-progressive lower dimensional dynamical patterns embedded in the higher dimensional HI space. Manifold learning on these HI point-cloud is envisaged by LandMark ISOMAP (L-ISOMAP) for isometric feature mapping. The dimensionality reduced L-ISOMAP descriptors are cascaded with stacked sparse autoencoder (SSAE) for learning deep textural feature and tumor malignancy detection thereof. Classification accuracy of 99.4% obtained on publicly available BreaKHis dataset outperforms the state-of-the-art methods and validates it's adequacy as an adjunct tool to clinicians in confirming their diagnosis. Further, employing L-Isomap based manifold embedding, the dimensionality of HI are reduced drastically without significant loss in its discriminating competency. These relieves the GPU requirement for SSAE aided deep learning. Experimental results are discussed in detail.