Neeraj Kumar - Deep learning for computational pathology

We strongly believe that the future of not only medical detection and diagnosis but also prognosis and treatment planning will be strongly influenced by pattern recognition and data analysis. Medical imaging will be no different, especially with the advent of techniques such as unsupervised feature extraction and deep learning aided by high performance computing (HPC) in the form of cloud clusters and GPU-based desktops. Currently, we are actively working on pattern recognition applications to histological images. Specifically, we have developed state-of-the art deep learning algorithms for nuclei and mitosis detection, epithelium vs. stroma classification, nuclear abnormality detection etc. In this talk, we will discuss about some of these algorithms and their role in deriving biological insights that can pave the way for improving our understanding of human carcinogenesis.

[@dr.mohammadimran5447]

Dear Prof.Dr.Neeraj Kumar IITG Sir, Awesome 👏👏👏👏 Hatsoff Sir What an Excellent Speech on Digital Pathology Image Analysis Great people like You will transform the world soon with this astounding research. Great Speech Sir.I have been looking forward to see more from you.I was looking for such research initiation for a long time. You did it.👏👏👏👏👌 Thanks a ton Dr.Neeraj Kumar Sir.

[@Sowegonomorearowing]

Great speech Neeraj. Is there a lot of focus on application of ML in healthcare in India?

[@neerajkumarsharma4415]

Yes Fortis, the focus is increasing day by day as more people are getting into the field. And thanks for the compliment.

[@Sowegonomorearowing]

Thanks for the reponse. The interest is pretty high in the US.

[@neerajkumarsharma4415]

Yes, I pretty much feel the same way. Are you working in this field as well?

[@maneeshmohan3001]

Thank you it was a great speech it helps me a lot as a biginner

[@a.gholiha6884]

great speech how can I contact the speaker?

[@neerajkumarsharma4415]

Hi, thank you for your appreciation. I can be reached at [e-mail]

[@augustine0890]

Would you share the code? Thank you so much.

[@shrekinasuit]

I would have listened to the entire talk hadn't there been so many factual errors in the introduction.....