Author

Abin Jose

RWTH Aachen UniversityORCID 0000-0002-3974-3552Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:clustering· 4 Approach:deep learning· 4 Research Focus:differential expression· 4 Research Focus:cell type annotation· 3 Approach:machine learning (classical)· 3 Research Focus:trajectory inference· 3 Assay:imaging· 2 Organism:human· 1

Frequent Co-Authors

Johannes StegmaieriD· 4 shared Rijo RoyiD· 3 shared Daniel Moreno-AndrésiD· 2 shared Dennis EschweileriD· 1 shared Fatemeh Javadian· 1 shared Gopal Avinash· 1 shared Ina Laube· 1 shared Maroof Abdul Aziz· 1 shared

Activity by Year

2022-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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4 shared papersBioinformatics

Johannes StegmaierORCID

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4 papers

Recent·Most cited

DEEP LEARNING APPROACH FOR RENAL CELL CARCINOMA DETECTION,SUBTYPING, AND GRADING

Maroof Abdul Aziz, Fatemeh Javadian, Sherin Susheel Mathew, Gopal Avinash, Johannes Stegmaier, Sonit Singh, Abin Jose

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

Proceedings - International Conference on Image Processing · Sep 27, 2024Peer-reviewedBioinformatics

Automatic Detection of Cell-cycle Stages using Recurrent Neural Networks

Abin Jose, Rijo Roy, Daniel Moreno-Andrés, Johannes Stegmaier

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

PLoS ONE · Mar 11, 2024Peer-reviewedBioinformatics

Weakly-supervised temporal segmentation of cell-cycle stages with center-cell focus using Recurrent Neural Networks

Abin Jose, Rijo Roy, Johannes Stegmaier

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.

Informatik aktuell · Jan 1, 2023Peer-reviewedBioinformatics

END-TO-END CLASSIFICATION OF CELL-CYCLE STAGES WITH CENTER-CELL FOCUS TRACKER USING RECURRENT NEURAL NETWORKS

Abin Jose, Rijo Roy, Dennis Eschweiler, Ina Laube, Reza Azad, Daniel Moreno-Andrés, Johannes Stegmaier

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.

bioRxiv · Nov 1, 2022 · PreprintBioinformatics