Saw this post trending on twitter and thought it might be a good resource for beginners / intermediates.

tweet: https://twitter.com/GokuMohandas/status/1202411040295645184

course: https://practicalai.me

The tweet has details on why the author changed from PyTorch to TensorFlow and it’s one of the few times I see concrete reasons for this specific use case. I’m an avid PyTorch dev (for research and production) but I’m willing to give TF a shot…but GOD I remember hating gradient tape.

Has anyone tried TF2.0 yet and liked it?

In reading the r/machinelearning posts I often feel that there are two communities here:

1. People who are interested in getting the last 2% of performance on their big data sets, using methods they can use *today*,
2. Researchers who are interested in discussing new ideas and algorithms, that may be only only demonstrated on MNIST or may not be practical for years to come.

I waste time skipping past comments from the other community (the one I’m not interested in), and sometimes find that I cannot tell even from the title of a thread which community it appeals to. Open the thread, scan, close, a minute wasted.

I suspect that explicitly recognizing these two communities would both save everyone a bit of time, but also might encourage some deeper conversations (at least on the research side – discussions won’t be buried with all the “doesn’t work as well as existing methods my real-world big dataset” comments).

There are already several ML related subreddits (LearnML, Deeplearning, ?). But this one appears to be the only one that attracts real researchers (I know that some of you are here), and if so is the only one that has this particular dichotomy.

What do you think?

Our paper “Keratinocytic Skin Cancer Detection on the Face using Region-based Convolutional Neural Network” was published on JAMA Dermatology. To my knowledge, the performance of cancer detection was compared with that of dermatologists for the first time in dermatology. Because most of previous studies were classification studies, preselection of end-user was essential. In addition, there were numerous false positives because training data set did not include enough number of common disorders and normal structures.

With the assistance of R-CNN, we trained neural networks with 1,106,886 image crops to localize and diagnose malignancy. The algorithm detects suspected lesion and shows malignancy score and predicts possible diagnosis (178 disease classes).

We used region-based CNN (faster-RCNN; backbone = VGG-16) as a region proposal module, and utilized CNN (SE-ResNet-50) to choose adequate lesion, and utilized CNN (SE-ResNeXt-50 + SENet) to determine malignancy. We chose a multi-step approach to reduce the dimension of problem (object detection -> classification).

The AUC for the validation dataset (2,844 images from 673 patients comprising 185 malignant, 305 benign, and 183 normal conditions) was 0.910. The algorithm’s F1 score and Youden index (sensitivity + specificity – 100%) were comparable with those of 13 dermatologists, while surpassing those of 20 non-dermatologists (325 images from 80 patients comprising 40 malignant, 20 benign, and 20 normal). We are performing an additional work with large scale external validation data set. The pilot result is similar with this report, so I hope I will publish soon.

Web DEMO (https://rcnn.modelderm.com) of the model is accessible via smartphone or PC, to facilitate scientific communication. Sorry for the slowness of the DEMO because it runs on my personal computer despite of the multi-threading and parallel processing with 2080 x1 and 1070 x1.

Thank you.

Paper : https://jamanetwork.com/journals/jamadermatology/article-abstract/2756346

Screenshot : https://i.imgur.com/2TCkdHf.png

Screenshot : https://i.imgur.com/IEZLfOg.jpg

DEMO : https://rcnn.modelderm.com

Anyone been able to find the conference and get on? In previous years they usually released the event on the app for the conference a week or so ahead of time. Haven’t seen anything about it this time around though.

I am trying to understand this paper which introduces StripNet. Nevertheless, due to the fact that the authors do not explain the steps really well, I’m having many troubles in understanding what they do.

1) Is ROIAlign just a Mask-R CNN? I do not really understand why they use it and it’s purpose.

2) After they partition the retinal layer region into 16pixel – wide partitions (why 16?), how do they classify the layers in each one?

Could someone who actually understands what’s going on in the paper help me? A simple explanation of the pipeline would be very helpful as I do have a ton of questions.

“Generating Diverse High-Fidelity Images with VQ-VAE-2” produce high quality and high resolution face image.

Does it beat state-of-the-art StyleGAN in face generation task? Thank you

I’m a junior doing a math/CS double major, and I need to pick some math electives.

Which 4 should I take:

Combinatorics

Mathematical Modeling

Graph Theory

Topology

Optimization

Ordinary Differential Equations 2

Intro to Partial Differential Equations