Abstract:

The microscopic assessment of tissue samples is instrumental for the diagnosis and staging of cancer, and thus guides therapy. However, these assessments demonstrate considerable variability and many regions of the world lack access to trained pathologists. Though artificial intelligence (AI) promises to improve the access and quality of healthcare, the costs of image digitization in pathology and difficulties in deploying AI solutions remain as barriers to real-world use. Here we propose a cost-effective solution: the augmented reality microscope (ARM). The ARM overlays AI-based information onto the current view of the sample in real time, enabling seamless integration of AI into routine workflows. We demonstrate the utility of ARM in the detection of metastatic breast cancer and the identification of prostate cancer, with latency compatible with real-time use. We anticipate that the ARM will remove barriers towards the use of AI designed to improve the accuracy and efficiency of cancer diagnosis.

https://www.nature.com/articles/s41591-019-0539-7.epdf?author_access_token=BI9AOTsesmNoV2lSdpucn9RgN0jAjWel9jnR3ZoTv0PDGU3ZwysZtsN41a2fOgaoj4PRxjTvAHjSFrKF_S_mq4QNNV8dNoxAjytIQuVz9vdjplLQHUSEPiIo392MzIJY8fqxLKHC5vIwNpLLEoXMnA%3D%3D

Hi everyone!

Few months ago I released Notify17, a tool to generate mobile notifications from simple web requests.

I think redditors of this sub may be interested in using it when dealing with models training, or any similar long lasting tasks, where you need to wait a long time between each iteration.

The most basic concept would be that, as soon as a model training finishes, you can invoke a function to let yourself know that the event occurred, and get the notification on your phone/browser:

from notify17 import n17_raw n17_raw("RAW_API_KEY", 'Model training finished') 

I’m looking for feedback from people in multiple fields, because I believe that Notify17 could be useful in many multiple scenarios than just backend development.

I’d love to know if anyone of you finds this tool to be useful for the machine learning field and/or what could be added/changed to make it worth the use for you all.

P.s. also MATLAB and LUA examples are around. In any case, a cURL request is enough to trigger any notification, e.g:

url -X POST  "https://hook.notify17.net/api/raw/RAW_API_KEY"  -F title="Model $MODEL_NAME training completed" 

Thanks a lot, I’m here to answer any question about this post!

Alberto

Hello everyone,

I was trying to train an autoencoder which takes a melspectrogram as input and outputs the same melspectrogram. It’s a reconstruction task. However, the model seems to be generating random noise. It’ll be great if anyone could point me towards any relevant github repos/papers which solve this task.

Thank you! 🙂

I have been playing around with DeOldify this past week, which as been a lot of fun. During my testing, I colorized a video of a Tasmanian Tiger which managed to reach the front page and resulted in an article from Daily Mail… which was awesome! Anyway, while playing around, I realized it might be cool to visualize the colorization process by colorizing a hyper-realistic drawing time lapse. The hope was that using this method I could see when the network recognized a face, hair, eyes, etc.

Achieving this was pretty simple as I didn’t need to write any code myself. I found a great time lapse from a talented artist drawing Cara Delevingne, downloaded it (with permission) and colorized it. I put the two videos side by side, and I was done!

Here are the results!

I hope you guys find it interesting. It was a little less eventful than I expected, but still pretty cool to see!

Hi,

I’d like to share the following toy dataset with you which is readily available in form as raw videos and tfrecords:

The dataset consists of 90 000 color videos that show a planar robot manipulator executing articulated manipulation tasks. More precisely, the manipulator grasps a circular object of random color and size and places it on top of a square object/platform of again random color and size. The initial configurations (location, size and color) of the objects were randomly sampled during generation. Different from other datasets such as the moving MNIST dataset, the samples comprise a goal-oriented task as described, making it more suitable for testing prediction capabilities of an ML model. For instance, one can use it as a toy dataset to investigate the capacity and output behavior of a deep neural network before testing it on real-world data.

https://github.com/ferreirafabio/PlanarManipulatorDataset

Feel free to use, share and comment 🙂

Recently I submitted my paper with a very novel but easy to replicate idea to the CIKM conference. However, it seems reviewers would like to reject the paper with any arbitrary reason they came up with. Because the idea is very simple to replicate, I am very worried about that they may steal my idea to submit to upcoming conferences. In addition to submitting to arxiv, what else can protect my idea from being stolen? Thanks!

https://youtu.be/H6Qiegq_36c

Current CNNs have to downsample large images before processing them, which can lose a lot of detail information. This paper proposes attention sampling, which learns to selectively process parts of any large image in full resolution, while discarding uninteresting bits. This leads to enormous gains in speed and memory consumption.