Pushpak Pati

Senior Scientist @ Johnson & Johnson

[Kevin Thandiackal @kevthandiackal]

Happy to share that our paper "Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images" has been accepted at #ECCV2022! 🎉

arXiv: https://t.co/0fbySOE3NR
Code: https://t.co/XW2uygm1xd

🧵1/6

[AK @_akhaliq]

Vision GNN: An Image is Worth Graph of Nodes
abs: https://t.co/uiQHKd2svW
github: https://t.co/9XCd8VYjWU

propose to represent the image as a graph structure and introduce a new Vision GNN (ViG) architecture to extract graph level feature for visual tasks

[Petar Veličković @PetarV_93]

Proud to share our 150-page "proto-book" with @mmbronstein @joanbruna @TacoCohen on geometric DL! Through the lens of symmetries and invariances, we attempt to distill "all you need to build the architectures that are all you need".

https://t.co/CBN0IG8BXR

More info below! 🧵

[Mara Graziani @mormontre]

Motivation Monday of an exciting week. Tomorrow 🕘 9-12 am: Building Interpretable AI for Digital Pathology @appliedmldays. Tickets from 30 CHF. Last chance to register to do some interpretability jazz with me, @pushpak_pati, @GuillaumeJaume and our special guest: Inti Zlobec

[Guillaume Jaume @GuillaumeJaume]

1/ Want to explore the paradigm of graphs in computational pathology?

Meet histocartography, a one-stop #python library to build, model and interpret entity-graphs!

https://t.co/7kYv2teTHZ

@IBMResearch @pushpak_pati @afoncubiertar @mariagabrani

More info 👇

[AMLD Intelligence Summit @appliedmldays]

On April 27th we look forward to hosting the workshop "Building Interpretable AI for Digital Pathology" led by @mormontre, @pushpak_pati and @GuillaumeJaume. Find out more here https://t.co/o3PNZaMMIp

[Pushpak Pati @pushpak_pati]

4/ Some papers already using BRACS:

- Hierarchical Graph Representations for Digital Pathology, https://t.co/3jUDSW7xdP
- Quantifying Explainers of Graph Neural Networks in Computational Pathology, CVPR, 2021, https://t.co/CDBzb38zMv

[Pushpak Pati @pushpak_pati]

1/ ⏰ The BRACS dataset is out!
Meet the large cohort of H&E stained breast cancer RoIs.

A joint effort by @istitutotumori, @ICAR_CNR & @IBMResearch
@GuillaumeJaume @afoncubiertar @mariagabrani

More info 👉 https://t.co/NmS1X4Y2vT & 👇

[Pushpak Pati @pushpak_pati]

3/ A challenging and realistic dataset that includes tumors of various sizes and aspect ratio, staining variation, tissue preparation artifacts.

[MedGIFT @MedGIFT_group]

1⃣Interpretable AI for Digital Pathology

🗓️27 April 9-12h (CET)
👩‍💻MSc, PhD students & researchers
💰69 (students) - 120 (rest) (whole event)
🗺️Online

@mormontre @pushpak_pati & @GuillaumeJaume will give you hands-on tutorials to apply interpretability

👉https://t.co/Kh8AoFV8OY

[Mara Graziani @mormontre]

Here a line up of encouraging activities on Interpretable #AI for these spring days!
Register to https://t.co/La3PL1J55m for attending the1⃣event: Building Interpretable AI for Digital Pathology

Me, @pushpak_pati and @GuillaumeJaume will be giving > 2 hours coding tutorials

[Guillaume Jaume @GuillaumeJaume]

Glad to announce that our work on Quantifying Explainers of Graph Neural Networks in Computational Pathology has been accepted to #CVPR2021!

@pushpak_pati @afoncubiertar @mariagabrani @IBMResearch

[Mara Graziani @mormontre]

Yay! Me, @pushpak_pati and @GuillaumeJaume will be talking about Interpretable AI for Digital Pathology for a half-day workshop at the @appliedmldays. Join us :)

[Petar Veličković @PetarV_93]

Alongside @pl219_Cambridge, I'll be teaching a Master's module on GNNs @Cambridge_Uni. Feels surreal to be on the (virtual) other side 😊

Based on @williamleif's book + my ongoing work w/ @joanbruna @mmbronstein @TacoCohen. Hope to release materials soon! https://t.co/bupyj89ACL

[Michael Bronstein @mmbronstein]

New year is a good time to is a good time to recap and make predictions. In a new post in @TDataScience I sought the opinion of 12 prominent researchers in the field of #GraphML to predict what is in store for 2021.

https://t.co/1wn0U2oVKR

[Xavier Bresson @xbresson]

My main talks on Graph Neural Networks in 2020

1. Introduction to GNNs
https://t.co/TmkJwFF3ds

2. Recent developments in GNNs
https://t.co/mter34Qutg

3. Benchmarking GNNs
https://t.co/fXz2zOllAC

Hope they can be useful.
Happy new year to everyone !

[Pushpak Pati @pushpak_pati]

@simongraham73 @TIAwarwick @PathLAKE_CoE @vqdang93 @nmrajpoot @sea_raza @mitkoveta @sakhurram @AI4Pathology @anantm @NormanZerbe @MTalhaQaiser @QiDou_ @jnkath @LawrenceYoung11 Awesome. This will be very useful for future developments in #digitalpathology.

[Computer Vision Lab Zurich @CVL_ETH]

We are excited to share our #NeurIPS2020 papers presented this week from our lab. Check the below teaser to get a glimpse of the topics. See you at the poster sessions!

https://t.co/Jy3QFOcc6K

[Pushpak Pati @pushpak_pati]

Checkout our new preprint https://t.co/zZ0zJM0UPM for quantifying explainability methods in #DigitalPathology

[Guillaume Jaume @GuillaumeJaume]

1/ Beyond saliency maps: How to quantify and rank #explainability methods in #digitalpathology?

Checkout our new preprint https://t.co/BoLTe71Y8G based on cell graph analysis.

@IBMResearch
@pushpak_pati
@afoncubiertar
@mariagabrani