Abin Jose

The paper predicts mitosis stages using a CNN followed by a GNN, incorporating timestamps as a graph to model internal interactions, achieving performance comparable to state-of-the-art methods.

This paper introduces an end-to-end deep learning pipeline for renal cell carcinoma detection, subtyping, and grading using The Cancer Genome Atlas data.

This paper proposes an RNN-based method to automatically detect mitosis stages from cell division videos, showing improved performance over traditional classifiers.

This study uses 3D CNN models to classify mitosis stages in 3D time-lapse cell sequences, achieving higher accuracy than previous methods.

This paper develops a weakly-supervised method using recurrent neural networks to classify cell-cycle stages in mitosis videos without annotated data, achieving performance comparable to supervised approaches.

This paper proposes an RNN-based method for automatic cell-cycle stage classification using time-lapse microscopy images, achieving superior performance over a classifier baseline.