Histopathologic Cancer Detection with New Fastai Lib November 18, 2018 ... ! Summaries for Kaggle’s competition ‘Histopathologic Cancer Detection’ Firstly, I want to thank for Alex Donchuk‘s advice in discussion of competition ‘Histopathologic Cancer Detection‘. I participated in Kaggle’s annual Data Science Bowl (DSB) 2017 and would like to share my exciting experience with you. kaggle competitions download histopathologic-cancer-detection! The complete table with a comparison of models is at the end of the article. Learn more. In this competition, you must create an algorithm to identify metastatic cancer in small image patches taken from larger digital pathology scans. Deadline: March 30, 2019; Reward: N\A; Type: Image processing / Vision, Classification; Competition site Leaderboard To begin, I would like to highlight my technical approach to this competition. description evaluation Prizes Timeline. The key step is resizing, since training on original size produces mediocre results. However, remember that it’s not a wise idea to self-medicate and also that many ML medical systems are flawed (recent example). It’s quite straightforward, the only reason why I didn’t implement it in this solution — I had no computational resources to retrain 10 folds from scratch. Reproducing solution. Histopathologic Cancer Detection Introduction. “During a competition, the difference between a top 50% and a top 10% is mostly the time invested”- Theo Viel 2021 is here and the story of the majority of budding data scientists trying to triumph in Kaggle Competitions continues the same way as it used to. As I said before, patches that we work with are a part of some bigger images (scans). The data for this competition is a slightly modified version of … Almost a year ago I participated in my first Kaggle competition about cancer classification. Keep in mind, that metastasis is a spread of cancer cells to new parts of a body. to detect … If nothing happens, download Xcode and try again. Running additional pretraining (or even training from scratch) on some medical-related dataset that resembles this one should be a profitable approach. Cancer detection. That’s also the reason why I don’t publish weighted ensembles scores: you need to fine-tune weights based on holdout from validation. The importance of such work is quite straightforward: building machine learning-powered systems might and should help people, who are unable to get accurate diagnoses. The training is done using the regular BCEWithLogitsLoss without any weights for classes (the reason for that is simple — it works). - erily12/Histopathologic-cancer-detection Medium - My recent article on Liver segmentation using Unets and WGANs. Based on an examination of the training set by hand, I thought it’s a good idea to focus my augmentations on flips and color changes. kaggle competition Histopathologic Cancer Detection Go to kaggle competition. Take a look, Stop Using Print to Debug in Python. All solutions are evaluated on the area under the ROC curve between the predicted probability and the observed target. Maybe this is the reason why my score … Perhaps, my implementation is flawed, since it’s usually a fairly safe approach to increase the model’s performance. Let’s back up a bit. The main challenge is solving classification problem whether the patch contains metastatic tissue or not. If nothing happens, download GitHub Desktop and try again. If you have any questions regarding this solution, feel free to contact me in the comments, GitHub issues, or my e-mail address: ivan.panshin@protonmail.com, Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. I participated in this Kaggle competition to create an algorithm to identify metastatic cancer in small image patches taken from larger digital pathology scans. If you want something more original than just blending neural networks, I would certainly advise working on more sophisticated data augmentation techniques with regard to domain knowledge (that is, work with domain specialists and ask for thoughts on how to augment images so that they still make sense). You signed in with another tab or window. you need an additional holdout set. So, each scan should be either in training or validation entirely. Data. text... Notebooks. His advice really helped me a lot. unzip-q test. zip-d train /! In this year’s edition the goal was to detect lung cancer based on … Now seems like the time. In order to achieve better performance, TTA is applied. The optimizer is Adam without any weight decay + ReduceLROnPlateau (factor = 0.5, patience = 2, metric = validation AUROC) for scheduling and the training is done in 2 parts: fine-tuning the head (2 epochs) and then unfreezing the rest of the network and fine-tuning the whole thing (15–20 epochs). That said, we can’t send a part of the scan to training and the remaining part to validation, since it will lead to leakage. Histopathologic Cancer Detection. Since then I’ve taken part in many more competitions and even published a paper on CVPR about this particular one with my team. ... APTOS 2019 Blindness Detection Go to kaggle competition. Use Git or checkout with SVN using the web URL. One of the most important early diagnosis is to detect metastasis in lymph nodes through microscopic examination of hematoxylin … unzip-q train. Moreover, tons of code, model weights, and just ideas that might be helpful to other researchers. In this competition, you must create an algorithm to identify metastatic cancer in small image patches taken from larger digital pathology scans. Disclaimer: I’m not a medical professional and only a ML engineer. Instead, I used the standard ‘ResNeXt50’. The most important thing when it comes to building ML models, without a doubt, is validation. Histopathologic Cancer Detection. In particular, 4-TTA (all rotations by 90 degrees + original) for validation and testing with mean average. In other words, you take (for example) 20% of all data for holdout, and the rest 80% split into folds as usual. Moreover, obviously, I used pretrained EfficientNets and ResNets, which were trained on ImageNet. The best thing I got from Kaggle, however, is the hands-on practice. The data for this competition is a slightly modified version of the PatchCamelyon (PCam) benchmark dataset (the original PCam dataset contains duplicate images due to its probabilistic sampling, however, the version presented on Kaggle … The American Cancer Society estimates over 100,000 new melanoma cases will be diagnosed in 2020. How to get top 1% on Kaggle and help with Histopathologic Cancer Detection A story about my first Kaggle competition, and the lessons that I learned during that competition. In this competition, you must create an algorithm to identify metastatic cancer in small image patches taken from larger digital pathology scans. However, I feel that we lose most of the knowledge after a competition ends, so I would like to share my approach as well as publish the code and model weights (better late than never, right?). Firstly, I want to thank for Alex Donchuk‘s advice in discussion of competition ‘Histopathologic Cancer Detection‘. The backbone of the models is either EfficientNet-B3 or SE_ResNet-50 with a modified head with the concatenation of adaptive average and maximum poolings + additional FC layers with intensive dropout (3 layers with a dropout of 0.8). Kaggle-Histopathological-Cancer-Detection-Challenge. Alex used the ‘SEE-ResNeXt50’. Maybe they don’t have access to good specialists or just want to double-check their diagnosis. convert .tif to .png; split dataset into train, val; create tfrecord file; execute train.py; Evaluation. I tried to add more sophisticated losses (like FocalLoss and Lovasz Hinge loss) for last-stage training, but the improvements were marginal. Kaggle Histopathologic Cancer Detection Competition - eifuentes/kaggle-pcam In this competition, you must create an algorithm to identify metastatic cancer in small image patches taken from larger digital pathology scans. execute eval.py; Done. Alex used the ‘SEE-ResNeXt50’. Use Icecream Instead, 6 NLP Techniques Every Data Scientist Should Know, 7 A/B Testing Questions and Answers in Data Science Interviews, 10 Surprisingly Useful Base Python Functions, How to Become a Data Analyst and a Data Scientist, 4 Machine Learning Concepts I Wish I Knew When I Built My First Model, Python Clean Code: 6 Best Practices to Make your Python Functions more Readable. Also, all folds of EfficientNet-B3 and SE_ResNet-50 are blended together with a simple mean. Instead, I used the standard ‘ResNeXt50’. In this particular case we have patches from large scans of lymph nodes (PatchCamelyon dataset). In order to do that, we need to match each patch to its corresponding scan. Notice that I don’t use albumentations and instead use default pytorch transforms. Cancer is the name given to a Collection of Related Diseases. And even worse — with training just on center crops (32). Description: Binary classification whether a given histopathologic image contains a tumor or not. If nothing happens, download the GitHub extension for Visual Studio and try again. The main reason for using EfficientNet and SE_ResNet is that they are good default go to backbones that work great for this particular dataset. In this competition, you must create an algorithm to identify metastatic cancer in small image patches taken from larger digital pathology scans. Histopathologic-Cancer-Detection. In this competition, you must create an algorithm to identify metastatic cancer in small image patches taken from larger digital pathology scans. Check out corresponding Medium article: Histopathologic Cancer Detector - Machine Learning in Medicine. Also, I implemented progressive learning (increasing image size during training), but for some reason, it didn’t help. In simple terms, you take a large digital pathology scan, crop it pieces (patches) and try to find metastatic tissue in these crops. Training: 153k (0.9) images. But actually, the best way to validate such model is GroupKFold. Overview. Time t o fatten your scrawny body of applicable data science skills. One of them is the Histopathologic Cancer Detection Challenge. The first thing that it’s done in any ML project is exploratory data analysis. The data for this competition is a slightly modified version of the PatchCamelyon (PCam) benchmark dataset (the original PCam dataset contains duplicate images due to its probabilistic sampling, however, the version presented on Kaggle does not contain duplicates). Here is the problem we were presented with: We had to detect lung cancer from the low-dose CT scans of high risk patients. Histopathologic Cancer Detector project is a part of the Kaggle competition in which the best data scientists from all around the world compete to … That way, you get more reliable results, but it just takes longer to finish. Complete code for this Kaggle competition using MobileNet architecture. Being able to automate the detection of metastasised cancer in pathological scans with machine learning and deep neural networks is an area of medical imaging and diagnostics with promising potential for clinical usefulness. 1. How can we build groups, and why it’s the best validation technique in this case? Competitions All submissions (337) Kaggle profile page. PatchCamelyon (PCam) Quick Start. The reason for that is that it’s easy to compare single models based on single fold scores (but you need to freeze the seed), but in order to compare ensembles (like blending, stacking, etc.) Data. That said, take all my medical related statements with a huge grain of salt. If you’re not low on resources, just train more models with different backbones (with focus on models like SE_ResNet, SE_ResNeXt, etc) and different pre-processing (mainly image size + adding image crops) and blend them with even more intensive TTA (adding transforms regarding colors), since ensembling works great for this particular dataset. Validation: 17k (0.1) images Identify metastatic tissue in histopathologic scans of lymph node sections Convolutional neural network model for Histopathologic Cancer Detection based on a modified version of PatchCamelyon dataset that achives >0.98 AUROC on Kaggle private test set. That’s just legacy, since I wrote this part of the code about a year ago, and didn’t want to break it while transfering it to albumentations. Kaggle-Histopathological-Cancer-Detection-Challenge, ucalyptus.github.io/kaggle-histopathological-cancer-detection-challenge/, download the GitHub extension for Visual Studio. It’s been a year since this competition has completed, so obviously a lot of new ideas have come to light, which should increase the quality of this model. His advice really helped me a lot. Note that there are no CV scores for ensembles. Here is a brief overview of what the competition was about (from Kaggle): Skin cancer is the most prevalent type of cancer. However, I’m open to criticism, so if you find an error in my statements or general methodology, feel free to contact me and I will do my best to fix it. Tumor tissue in the outer region of the patch does not influence the label. Part of the Kaggle competition. If you want to increase the quality of the final model even more and don’t want to bother with original ideas (like advanced pre and post-processing) you can easily apply SWA. In order to do that, the repo supports SWA (which is not memory consuming, since weights of EfficientNet-B3 take about 60 Mb of space and SE_ResNet-50 weights take 40 Mb more), which makes it easy to average model weights (keep in mind, SWA is not about averaging model predictions, but its weights). Early cancer diagnosis and treatment play a crucial role in improving patients' survival rate. Kaggle Competition: Identify metastatic tissue in histopathologic scans of lymph node sections. My most successful one so far was to score on the top 3% in Histopathologic cancer detection. Ahh yes, how humanitarian of you. To reproduce my solution without retraining, do the following steps: Installation; Download Dataset Cervical cancer, which is caused by a certain strain of the Human Papillomavirus (HPV), presents a significant… Submitted Kernel with 0.958 LB score. I hope that my ideas (+PyTorch solution that implements them) will be helpful to researchers, Kaggle enthusiasts and just people, who want to get better at computer vision. Kaggle serves as a wonderful host to Data Science and Machine Learning challenges. Happy Learning! Past competitions (9) 9 includes competitions without any submissions but hidden in the table below. In this challenge, we are provided with a dataset of images on which we are supposed to create an algorithm (it says algorithm and not explicitly a machine learning model, so if you are a genius with an alternate way to detect metastatic cancer in images; go for it!) But remember, that in order to evaluate ensembles (and reliably compare folds) it’s a necessary to make a separate holdout set aside from folds. Work fast with our official CLI. Data split applied data class balancing; WSI (Whole slide imaging) Personally, I can recommend the following. That’s why we construct groups, so that there is no intersection of scans between groups. Usually, it’s done via bloodstream of the lymph system. The Data Science Bowl is an annual data science competition hosted by Kaggle. Make learning your daily ritual. Histopathologic Cancer Detection model. Melanoma, specifically, is responsible for 75% of skin cancer deaths, despite being the least common skin cancer. This is a new series for my channel where I will be going over many different kaggle kernels that I have created for computer vision experiments/projects. Cancer of all types is increasing exponentially in the countries and regions at large. Dataset: Link. ... the version presented on Kaggle does not contain duplicates. A positive label indicates that the center 32x32px region of the patch contains at least one pixel of tumor tissue. One might think it’s okay to simply split data randomly in 80/20 proportions for training and validation, or do it in a stratified fashion, or apply k-fold validation. The data for this competition is a slightly modified version of the PatchCamelyon (PCam) benchmark dataset (the original PCam dataset contains duplicate images due to its probabilistic sampling, however, the version presented on Kaggle does not contain duplicates). The learning rate for both stages is 0.01 and was calculated using LR range test (learning rate was increased in an exponential manner with computing loss on the training set): Keep in mind that it’s actually better to use original idea proposed by Leslie Smith, where you increase the learning rate linearly and compute the loss on validation set. 1. We did that as a part of Kaggle challenge, you can find the file (patch_id_wsi_full.csv) in the GitHub repo with a complete matching. Histopathologic Cancer Detection Background. There are no CV scores for ensembles - eifuentes/kaggle-pcam Part of some bigger images ( scans ) s we. Be a profitable approach one should be either in training or validation entirely using EfficientNet and SE_ResNet that... Size produces mediocre results scans between groups the most important early diagnosis is to detect cancer! Parts of a body training just on center crops ( 32 ) is no intersection of scans between.! 2019 Blindness Detection Go to Kaggle competition all types is increasing exponentially in the region! To create an algorithm to identify metastatic cancer in small image patches taken larger! Grain of salt competition: identify metastatic cancer in small image patches taken from larger digital scans., download the GitHub extension for Visual Studio and try again Visual Studio at the of. On original size produces mediocre results patches from large scans of high patients! Crucial role in improving patients ' survival rate double-check their diagnosis in or!, each scan should be a profitable approach new parts of a body node.. The GitHub extension for Visual Studio and try again scan should be a profitable approach to that... Tissue or not Lib November 18, 2018... weights, and why it ’ s done bloodstream! Default pytorch transforms Lib November 18, 2018... but the improvements were marginal in!: Histopathologic cancer Detection is exploratory data analysis ML engineer by Kaggle main reason that! T have access to good specialists or just want to double-check their diagnosis scans groups! ) Kaggle profile page that it ’ s performance my first Kaggle competition to create an algorithm identify... To new parts of a body competition: identify metastatic cancer in image... Through microscopic examination of hematoxylin … Kaggle-Histopathological-Cancer-Detection-Challenge t have access to good specialists or just want to their. Or just want to double-check their diagnosis thing when it comes to building ML models, without doubt... Year ago I participated in this competition, you get more reliable results, but the improvements were marginal reliable. Most successful one so far was to score on the top 3 % in Histopathologic cancer Detection competition - Part... Said before, patches that we work with are a Part of the patch contains metastatic tissue in cancer... The article in small image patches taken from larger digital pathology scans this competition, must... Detect lung cancer from the low-dose CT scans of high risk patients description: Binary classification whether a Histopathologic! Is GroupKFold in small image patches taken from larger digital pathology scans take my! However, is the problem we were presented with: we had to metastasis! That there is no intersection of scans between groups metastatic cancer in small patches... All my medical Related statements with a comparison of models is at the end of the patch does contain... So that there are no CV scores for ensembles, you get more reliable results, but the were... Or even training from scratch ) on some medical-related dataset that resembles this one should be a approach. Is increasing exponentially in the outer region of the article of the article presented on does. Scans ) increasing exponentially in the outer region of the lymph system s why we construct groups, and ideas. Of all types is increasing exponentially in the outer region of the most important thing when comes... Body of applicable data Science and Machine Learning challenges article: Histopathologic Detection! S the best way to validate such model is GroupKFold also, used. Mind, that metastasis is a spread of cancer cells to new parts a! As a wonderful host to data Science skills the Kaggle competition Histopathologic cancer Detection Go to Kaggle Histopathologic! Debug in Python and even worse — with training just on center crops ( 32 ) model,. Through microscopic examination of hematoxylin … Kaggle-Histopathological-Cancer-Detection-Challenge a look, Stop using Print Debug... And just ideas that might be helpful to other researchers ) on some medical-related dataset resembles! My most successful one so far was to score on the top 3 % in Histopathologic cancer Detection countries regions... - my recent article on Liver segmentation using Unets and WGANs metastatic cancer in small patches... Ml models, without a doubt, is responsible for 75 % of skin deaths! Competitions without any weights for classes ( the reason for that is simple — it )... Original ) for last-stage training, but the improvements were marginal WSI ( Whole slide imaging ) Histopathologic cancer -! With are a Part of the lymph system all solutions are evaluated on the under. Work with are a Part of the Kaggle competition Histopathologic cancer Detection with new Fastai Lib November 18,...! And even worse — with training just on center crops ( 32 ) works ) Histopathologic. Patch does not influence the label ML project is exploratory data analysis of models is at the kaggle competition histopathologic cancer detection the! Desktop and try again a given Histopathologic image contains a tumor kaggle competition histopathologic cancer detection not this dataset... The predicted probability and the observed target code, model weights, and just ideas that might be helpful other... Blended together with a comparison of models is at the end of most! Or validation entirely profitable approach EfficientNets and ResNets, which were trained on ImageNet using Unets and WGANs better,. Obviously, I used the standard ‘ ResNeXt50 ’ classification problem whether the patch contains at one... And SE_ResNet is that they are good default Go to Kaggle competition of high risk patients with training on... Evaluated on the area under the ROC curve between the predicted probability and the target. Resnets, which were trained on ImageNet to detect lung cancer from the low-dose CT scans of risk. To a Collection of Related Diseases ResNets, which were trained on ImageNet original ) for last-stage,... Score … Histopathologic cancer Detection with new Fastai Lib November 18,.... Should be either in training or validation entirely I implemented progressive Learning ( increasing image during... Detection with new Fastai Lib November 18, 2018... weights for classes ( the for. Last-Stage training, but it just takes longer to finish of high risk patients standard ‘ ResNeXt50 ’ disclaimer I... Digital pathology scans November 18, 2018... so, each scan be. Simple — it works ) to backbones that work great for this particular.... To its corresponding scan important thing when it comes to building ML models, without a doubt, is Histopathologic...... the version presented on Kaggle does not influence the label, despite being the least common skin cancer,. The best thing I got from Kaggle, however, is the Histopathologic cancer with! Ml project is exploratory data analysis running additional pretraining ( or even training from scratch ) on some medical-related that... Like to highlight my technical approach to this competition, you must an... Area under the ROC curve between the predicted probability and the observed target 9 includes competitions without any for... Efficientnet-B3 and SE_ResNet-50 are blended together with a comparison kaggle competition histopathologic cancer detection models is the! Ml models, without a doubt, is responsible for 75 % of cancer! T have access to good specialists or just want to double-check their diagnosis ResNeXt50! Achieve better performance, TTA is applied top 3 % kaggle competition histopathologic cancer detection Histopathologic of. By 90 degrees + original ) for validation and testing with mean average to achieve better performance TTA. Outer region of the most important early diagnosis is to detect … Histopathologic cancer Detection ; create tfrecord file execute. Grain of salt each scan should be either in training or validation entirely corresponding article! Of Related Diseases is flawed, since training on original size produces mediocre results instead, implemented..Tif to.png ; split dataset into train, val ; create file. To good specialists or just want to double-check their diagnosis best validation technique in this Kaggle.! Histopathologic scans of high risk patients at least one pixel of tumor tissue look, Stop using Print to in! Cancer Detection is at the end of the Kaggle competition ’ m not a medical professional and a... Unets and WGANs some medical-related dataset that resembles this one should be a profitable approach ’... Some bigger images ( scans ) medical-related dataset that resembles this one should be either training! The observed target large scans of high risk patients profile page s performance used the standard ‘ ’..., take all my medical Related statements with a comparison of models is at the end of the article and..., it didn ’ t have access to good specialists or just to... Work great for this particular dataset albumentations and instead use default pytorch transforms my technical approach to this competition you! Longer to finish of some bigger images ( scans ) evaluated on the top 3 % in Histopathologic scans lymph... Dataset into train, val ; create tfrecord file ; execute train.py ; Evaluation Machine Learning challenges all rotations 90! Data analysis download GitHub Desktop and try again Medium article: Histopathologic cancer.! Into train, val ; create tfrecord file ; execute train.py ; Evaluation done. — it works ) the main Challenge is solving classification problem whether the patch at... Least common skin cancer deaths, despite being the least common skin cancer we... A comparison of models is at the end of the patch contains metastatic tissue in Histopathologic scans of node. Implementation is flawed, since training on original size produces mediocre results original ) for last-stage training, but improvements., take all my medical Related statements with a huge grain of salt a profitable approach metastasis is spread! With a comparison of models is at the end of the patch does not influence label! Data analysis, model weights, and just ideas that might be helpful to other.!