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

There are so many different optimizers to choose from when doing ML projects and it was never clear to me which one should be used. So I decided to do a little research project and see for myself how they compared. I’ve written up the results here. Code: https://github.com/rickwierenga/BenchmarkingMLOptimizers

Let me know if you have any questions/thoughts.

Blog: https://blog.marekrosa.org/2019/12/badger.html

Paper: https://arxiv.org/abs/1912.01513

Badger = an architecture and a learning procedure where:

An agent is made up of many experts

All experts share the same communication policy (expert policy), but have different internal memory states

There are two levels of learning, an inner loop (with a communication stage) and an outer loop

Inner loop – Agent’s behavior and adaptation emerges as a result of experts communicating between each other. Experts send messages (of any complexity) to each other and update their internal memories/states based on observations/messages and their internal state from the previous time-step. Expert policy is fixed and does not change during the inner loop.

Inner loop loss need not even be a proper loss function. It can be any kind of structured feedback guiding the adaptation during the agent’s lifetime.

Outer loop – An expert policy is discovered over generations of agents, ensuring that strategies that find solutions to problems in diverse environments can quickly emerge in the inner loop.

Agent’s objective is to adapt fast to novel tasks

Exhibiting the following novel properties:

Roles of experts and connectivity among them assigned dynamically at inference time

Learned communication protocol with context-dependent messages of varied complexity

Generalizes to different numbers and types of inputs/outputs

Can be trained to handle variations in architecture during both training and testing

Hey r/MachineLearning question for you guys

Given a series of bartering transactions, example below:

• Item 1 was traded for Item 2 & Item 3
• Item 4 was traded for Item 5 & Item 6
• Item 3 was traded for Item 7
• Item 7 was traded for Item 8
• etc

What algorithm could you use to determine the relative value of each item?

Example from above:

• Item 1 was traded for 2 & 3, this would imply that item 1 roughly equal to the value 2 & 3 combined (and more valuable than either one individually)
• Item 7 was traded for Item 3 (1 for 1 trade) which would imply they have the same value

Now if I had say 300 items, and 1000s of “transaction” data points for the whole system, what’s the best way to determine the relative value of the group?

Example Questions:

• How much is Item 1 (.00 – 1.00) worth compared to Item 3?
• How much is Item 1 worth compared to the rest of the pack?

Bonus Points: A python package would be awesome.

https://github.com/ozanzgur/mlpl

This is a project that I have been working on to automate testing my experiments in data science projects. It also involves some automated experiments. However, there is still a lot of work to be done. I will be writing unit tests and making some parts simpler. It can work with tensorflow and lgbm, but some work needs to be done before that.

Would you consider using such tools in your projects? What features should I add? Any advice is greatly appreciated.

Hey all,

Does anyone have experience feeding keras multiband TIF files? We have 7-band, 8-bit, 74×74 TIF files and for whatever reason it seems like pillow can’t recognize them. 3-band TIFs work just fine.

OSError: cannot identify image file ‘/content/drive/My Drive/raster/Twin_Cities_EW_Data/train/1/landsat_twin_cities_2000_with_no_data_values10149.TIF’

We’ve gone into the deep end, updating pillow to the most recent version. Trying all matter of TIF parameter changes. etc. etc.

Any insight is appreciated.