Philip Chikontwe, PhD

Research Fellow • @Harvard | @harvardmed | @HarvardDBMI | Machine Learning for Computational Pathology | Yu Lab

[Dima Vremenko @DVremenko]

WHAT? @ChatGPT can make phone calls? With @inkbox_ai it can do that and way more!

Try our new plugin: https://t.co/KBN70teuUV

[Kun-Hsing Yu @kunhsingyu]

Excited to share that our paper on a foundation AI model for cancer prognosis and pathology diagnosis is published in Nature today. https://t.co/HShXzxVeZE @HarvardDBMI @NIH @NIGMS @CDMRP @AmericanCancer #AI #DigitalPathology

[Philip Chikontwe, PhD @philip_chicco]

@K_Neeil Congratulations!

[MICCAI Society @MICCAI_Society]

Poster Highlights: The Audience's Choice. We want to hear your opinion on which posters stood out to you. Cast your vote about today's posters by 7 pm, make your opinion count. Visit link:
https://t.co/3c9R8ECaS9

[MICCAI Society @MICCAI_Society]

Welcome to #MICCAI2023 in Vancouver Canada! The Opening Ceremonies are about to begin! We are looking forward to an amazing conference!
@MiccaiStudents @WomenInMICCAI @RMiccai

[MICCAI Society @MICCAI_Society]

#MICCAI2023 is almost here. Have you reviewed the Program Book? It's 119 pages full of information about key notes, orals, poster sessions and more. It's worth reviewing before the conference starts. See you soon!
https://t.co/6pssUUGsBZ
@WomenInMICCAI @MiccaiStudents @RMiccai

[Hz FU @Hyzz_Hoo]

[Call for Papers!] - IEEE Journal of Biomedical and Health Informatics (JBHI, if 7.7) Special Issue on "Foundation Models in Medical Imaging".

Very happy to serve as Guest Editor for this Special Issue with
@XiaoxiaoLi8 @ja_schnabel @CaroPetitjean Jiong Zhang

[AK @_akhaliq]

DreamFusion: Text-to-3D using 2D Diffusion
paper: https://t.co/euVCkNPzB0
abs: https://t.co/FO7nqjLrQA
project page: https://t.co/BTvt1WQqIi

DeepDream on a pretrained 2D diffusion model enables text-to-3D synthesis

[Jinwei Ye @jinwei_ye]

First time attending CVPR? Join the CVPR Academy on Monday morning! #CVPR2022
Program: https://t.co/CKCPvqjCUC
Time: June 20, 9:30am - 1:30pm (CDT)
Location: Room 202 & Virtual

[yobibyte @y0b1byte]

First year as a PhD student...

[Florian Jug @florianjug]

🚨 ICLR SPOTLIGHT 🚨
Unsupervised denoising, i.e. removing noise in data without having ground truth, has a new state-of-the-art. A short 🧵 about why this rocks...
https://t.co/jPjfHWEkK1
Spoiler: it can also remove structured noise & gives diverse solutions for ambiguous data

[Alexey Bochkovskiy @alexeyab84]

DPT (Dense Prediction Transformers) is accepted to ICCV2021 - State of the art Real-time (>30 FPS) neural network for Semantic segmentation and Mono-Depth estimation from one RGB image
Paper: https://t.co/igu5TOqYxN
Code: https://t.co/0GFfJpqLCG

[Medical Computer Vision Workshop @ CVPR2023 @MedComVision]

The MCV organization team - @tiqerlu, @arbtal, @CammaLab, @lena_maierhein, @QiDou_, @bazile - looks forward to welcoming you. More information for the workshop schedule at: https://t.co/gSVWlwNDWp (June 20th in conjunction with @CVPR / @CVPRConf 2021)
#CVPR2021

[Maria Brbic @mariabrbic]

What is needed for generalizable and interpretable few-shot learning methods applicable across many domains?
We advocate that the answer lies in concept-based reasoning!

Check our paper at #ICLR2021 poster session today, 5-7pm PDT! https://t.co/Vs044tlGFy
w/ @kaidicao and @jure

[Pedro C. Neto @Pedro18_Neto]

Are you a researcher working with Computational Pathology? Then check out the CDpath workshop @ICCV_2021 about the integration of #computervision and #AI in digital pathology workflow.
Visit https://t.co/D5mnTwkCTX for details on submission and deadlines.

[Yuyin Zhou @yuyinzhou_cs]

Excited to announce our @ICCV_2021 workshop "Computer Vision for Automated Medical Diagnosis"!

https://t.co/5XZ9HcS2d8

Co-organized with @ylqzd2011 @realSharonZhou @maithra_raghu @QiDou_ @YuankaiHuo @holgerrroth Bennett, Dinggang, Ronald, Lei, @mattlungrenMD @YuilleAlan

[Philip Chikontwe, PhD @philip_chicco]

@AICoffeeBreak You only need a +1% improvement to beat him.

[Aran Komatsuzaki @arankomatsuzaki]

Contrasting Contrastive Self-Supervised Representation Learning Models

Examines over 30 encoders, 4 pre-training datasets and 20 downstream tasks to address various questions such as the perf of SSL models compared to their supervised counterparts.

https://t.co/jvA8GAenHB

[Kalon Overholt @kalon_overholt]

If you want to make six figures overnight have you ever tried being a grad student???

[Mark⚡️Heckler 👨🏻‍💻🛩️ @mkheck]

https://t.co/0PK0sFfF8C

"Why Senior Engineers Hate Coding Interviews"

Great read, great points.