An open-source project for applying deep learning to medical scenarios

Smaranjit Ghose

Last update: May 29, 2022

Related tags

Deep Learning python tensorflow medical-imaging deeplearning hacktoberfest slop streamlit ch-20 slop20

Overview

Auto Vaidya

An open source solution for creating end-end web app for employing the power of deep learning in various clinical scenarios like implant detection, pneumonia detection, brain mri segmentation etc.

Suggestions for PR:

Please give your PR for the test branch unless requested otherwise by the project maintainer
Name your PR appropiately
Ensure that you had already raised an issue for this PR and the project maintainer had approved and assigned you
In the PR description, typically the following are expected:
- Dataset Used:
- Dataset Size:
- Dataset Source:
- Link to Colab Notebook: Please ensure you give access for view to anyone with link
- Your Exploratory Data Analysis [Snapshots of the relevant ones and your inference from that]
- Any Pre-Processing methods used. [Elaborate on them]
- Your framework to train
- Different methods used for training
- Test/Train Split
- Results: Please do not simply state test accuracy. Other perfomance metrics like F1 score,etc are expected
- ** Draw a table to show the comparitive analysis of the performance of the different methods you used
- Conclusion: Which method you think is best and why?
A copy of the notebook used for your training is expected inside the notebooks/ directory.
Please name the notebook as name_of_the_problem_your_github_username
The model files are expected to be inside a models\name_of_your_problem\ directory
If you are using TensorFlow 2.0, please give both the h5 as well as saved_model file
Once your PR, gets approved uptil this, proceed with a follow up pr to integrate it inside the streamlit app. Refer this if you are unaware of how to use streamlit and host it
For the streamlit app, it would be a good practice if you define the function for classification/prediction/regression inside a separate python file say your_problem_name.py and import it inside app.py ( Believe me this would save a lot of time otherwise wasted in debugging)
For the second PR, you are expected to do the above changes and provide screenshots/a small clip of the working model of the app after integrating your model from the previous PR
For the second PR, it should be one the test branch only, later the project maintainers will merge it with the master branch for a stable release
For PRs, related to frontend please give it to the frontend branch
Once accepted, give a follow up PR to the test branch to render your html,css files for a page using streamlit
As stated above you are expected to give screenshots, descriptions and other details for the PR

Entire App on Heroku: https://auto-vaidya.herokuapp.com/ Frontend on Netlify: autovaidya.netlify.app

Comments

Adding Feedback Page

Adding Feedback Page

As we know that feedback plays a very good role in knowing any loopholes or how can we improve our work. I want to take up this issue by creating a feedback page for our project. @smaranjitghose I want to work on this.
enhancement CH-20

opened by sharur7 9
Added Model for detecting Skin Cancer

Hello Sir, I have trained a deep learning model ( mobile-net ) to detect the type of Skin Cancer ( having 7 classes ). Data-set used is skin-cancer-mnist-ham10000. The link to colab is - link. The saved model is present in saved_models directory.
deprecated

opened by SoniGarima 7
Add the pricing page

Hello there, As I can see that there is a requirement for the Pricing page but no one has actually worked on the page. So, I would love to work on page design and development. :)

opened by dipanshparmar 5
Diabetes_Prediction

jupyter notebook link: https://colab.research.google.com/drive/1Op49nQIa-nJn42Ibczznv5IKMIlyhBXy?usp=sharing

KNN: train accuracy 0.8206106870229007 auc 0.6750868751670676 test accuracy 0.7461538461538462 Precision 0.6666666666666666 Recall 0.46511627906976744 F1 Score 0.547945205479452 [0.81132075 0.81132075 0.69230769 0.80769231 0.80769231] 0.786066763425254

LOGISTIC REGRESSION: Training set accuracy: 0.802 Test set accuracy: 0.723 train accuracy 0.8015267175572519 auc 0.6750868751670676 test accuracy 0.7461538461538462 Precision 0.6666666666666666 Recall 0.46511627906976744 F1 Score 0.547945205479452 [0.83018868 0.75471698 0.73076923 0.80769231 0.84615385] 0.7939042089985487

DECISION TREE Accuracy on training set: 0.851 Accuracy on test set: 0.762 train accuracy 0.8015267175572519 auc 0.6750868751670676 test accuracy 0.7461538461538462 Precision 0.6666666666666666 Recall 0.46511627906976744 F1 Score 0.547945205479452 [0.83018868 0.75471698 0.73076923 0.80769231 0.84615385] 0.7939042089985487

SVM : Accuracy on training set: 0.824 Accuracy on test set: 0.715 auc 0.628575247260091 Precision 0.6153846153846154 Recall 0.37209302325581395 F1 Score 0.463768115942029

SVM Cross_val: train_accuracy: 0.7935897435897437 Test accuracy: 0.738 {'clf__C': 1000.0, 'clf__gamma': 0.001, 'clf__kernel': 'rbf'}
deprecated

opened by amoghatsunil 4
Skin cancer detection
Sir, The PR is for issue #51 The data-set I have used for training and testing the model is skin-cancer-mnist-ham10000. This data-set contains 10015 unique images of 7 types of skin cancer. The link to the data-set is here. Here's the link to colab notebook. I have used the mobilenet model with some trainable layers and some already trained on 'imagenet' database. Apart, I have trained it using 2 different ways:

Simply on whole dataset with 10 epochs --> gave accuracy of about 75% on validation set.

Used data augmentation ( using ImageDataGenerator API provided by Keras) on dataset with 40 epochs of training --> gave 80% accuracy on validation set.

I have used fit_generator method of Keras API to train the model and for generating the dataset from image folders, I chose flow_from_directory method of ImageDataGenerator with many parameters for data augmentation. I have split the data into 90% train data and 10% test data using train_test_split method of sklearn module. I found second model more convincing because it is trained on more number of images and more number of times. Besides, The evaluation metrics of second model is as follows: accuracy: 0.8042 Precision = 0.81 Recall = 0.80 F1-Score = 0.804
PR: merged CH-20 Medium
opened by SoniGarima 3
Designed the landing page
The PR is for issue #57 I have added the following:

Logo

Slogan

Names of all the classifiers included in this project ( Can add more later)

Video illustration on how to use this web application

PR: reviewed-approved PR: merged Hacktoberfest
opened by SoniGarima 2
Change Font Family and Improve Navbar
Currently, the font-families used aren't so exciting for the project. Use the appropriate combination to add some more flare to the project.

You can use tools like :

https://fontpair.co/

https://fonts.google.com/

After that design a new Navbar for the application, which satisfy the following requirements:

The navbar must need to have a white background and a box-shadow on the bottom and must stick to the top at all times.

Use appropriate colors (new-pallet) and set-up as CSS-Variables for people to use again.

The nav links on hover need to change color and add a small underline effect.

The navbar must be fully responsive on all screens.

CH-20 Easy Medium UI/UX
opened by abhu-A-J 2
Bump pillow from 9.0.1 to 9.3.0
Bumps pillow from 9.0.1 to 9.3.0.

Release notes

Sourced from pillow's releases.

9.3.0

https://pillow.readthedocs.io/en/stable/releasenotes/9.3.0.html

Changes

Initialize libtiff buffer when saving #6699 [@radarhere]

Limit SAMPLESPERPIXEL to avoid runtime DOS #6700 [@wiredfool]

Inline fname2char to fix memory leak #6329 [@nulano]

Fix memory leaks related to text features #6330 [@nulano]

Use double quotes for version check on old CPython on Windows #6695 [@hugovk]

GHA: replace deprecated set-output command with GITHUB_OUTPUT file #6697 [@nulano]

Remove backup implementation of Round for Windows platforms #6693 [@cgohlke]

Upload fribidi.dll to GitHub Actions #6532 [@nulano]

Fixed set_variation_by_name offset #6445 [@radarhere]

Windows build improvements #6562 [@nulano]

Fix malloc in _imagingft.c:font_setvaraxes #6690 [@cgohlke]

Only use ASCII characters in C source file #6691 [@cgohlke]

Release Python GIL when converting images using matrix operations #6418 [@hmaarrfk]

Added ExifTags enums #6630 [@radarhere]

Do not modify previous frame when calculating delta in PNG #6683 [@radarhere]

Added support for reading BMP images with RLE4 compression #6674 [@npjg]

Decode JPEG compressed BLP1 data in original mode #6678 [@radarhere]

pylint warnings #6659 [@marksmayo]

Added GPS TIFF tag info #6661 [@radarhere]

Added conversion between RGB/RGBA/RGBX and LAB #6647 [@radarhere]

Do not attempt normalization if mode is already normal #6644 [@radarhere]

Fixed seeking to an L frame in a GIF #6576 [@radarhere]

Consider all frames when selecting mode for PNG save_all #6610 [@radarhere]

Don't reassign crc on ChunkStream close #6627 [@radarhere]

Raise a warning if NumPy failed to raise an error during conversion #6594 [@radarhere]

Only read a maximum of 100 bytes at a time in IMT header #6623 [@radarhere]

Show all frames in ImageShow #6611 [@radarhere]

Allow FLI palette chunk to not be first #6626 [@radarhere]

If first GIF frame has transparency for RGB_ALWAYS loading strategy, use RGBA mode #6592 [@radarhere]

Round box position to integer when pasting embedded color #6517 [@radarhere]

Removed EXIF prefix when saving WebP #6582 [@radarhere]

Pad IM palette to 768 bytes when saving #6579 [@radarhere]

Added DDS BC6H reading #6449 [@ShadelessFox]

Added support for opening WhiteIsZero 16-bit integer TIFF images #6642 [@JayWiz]

Raise an error when allocating translucent color to RGB palette #6654 [@jsbueno]

Moved mode check outside of loops #6650 [@radarhere]

Added reading of TIFF child images #6569 [@radarhere]

Improved ImageOps palette handling #6596 [@PososikTeam]

Defer parsing of palette into colors #6567 [@radarhere]

Apply transparency to P images in ImageTk.PhotoImage #6559 [@radarhere]

Use rounding in ImageOps contain() and pad() #6522 [@bibinhashley]

Fixed GIF remapping to palette with duplicate entries #6548 [@radarhere]

Allow remap_palette() to return an image with less than 256 palette entries #6543 [@radarhere]

Corrected BMP and TGA palette size when saving #6500 [@radarhere]

... (truncated)

Changelog

Sourced from pillow's changelog.

9.3.0 (2022-10-29)

Limit SAMPLESPERPIXEL to avoid runtime DOS #6700 [wiredfool]

Initialize libtiff buffer when saving #6699 [radarhere]

Inline fname2char to fix memory leak #6329 [nulano]

Fix memory leaks related to text features #6330 [nulano]

Use double quotes for version check on old CPython on Windows #6695 [hugovk]

Remove backup implementation of Round for Windows platforms #6693 [cgohlke]

Fixed set_variation_by_name offset #6445 [radarhere]

Fix malloc in _imagingft.c:font_setvaraxes #6690 [cgohlke]

Release Python GIL when converting images using matrix operations #6418 [hmaarrfk]

Added ExifTags enums #6630 [radarhere]

Do not modify previous frame when calculating delta in PNG #6683 [radarhere]

Added support for reading BMP images with RLE4 compression #6674 [npjg, radarhere]

Decode JPEG compressed BLP1 data in original mode #6678 [radarhere]

Added GPS TIFF tag info #6661 [radarhere]

Added conversion between RGB/RGBA/RGBX and LAB #6647 [radarhere]

Do not attempt normalization if mode is already normal #6644 [radarhere]

... (truncated)

Commits

d594f4c Update CHANGES.rst [ci skip]

909dc64 9.3.0 version bump

1a51ce7 Merge pull request #6699 from hugovk/security-libtiff_buffer

2444cdd Merge pull request #6700 from hugovk/security-samples_per_pixel-sec

744f455 Added release notes

0846bfa Add to release notes

799a6a0 Fix linting

00b25fd Hide UserWarning in logs

05b175e Tighter test case

13f2c5a Prevent DOS with large SAMPLESPERPIXEL in Tiff IFD

Additional commits viewable in compare view

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Dependabot commands and options

You can trigger Dependabot actions by commenting on this PR:

@dependabot rebase will rebase this PR

@dependabot recreate will recreate this PR, overwriting any edits that have been made to it

@dependabot merge will merge this PR after your CI passes on it

@dependabot squash and merge will squash and merge this PR after your CI passes on it

@dependabot cancel merge will cancel a previously requested merge and block automerging

@dependabot reopen will reopen this PR if it is closed

@dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually

@dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)

@dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)

@dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)

@dependabot use these labels will set the current labels as the default for future PRs for this repo and language

@dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language

@dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language

@dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

You can disable automated security fix PRs for this repo from the Security Alerts page.

PR: unreviewed dependencies
opened by dependabot[bot] 0
Bump tensorflow-cpu from 2.7.2 to 2.9.3
Bumps tensorflow-cpu from 2.7.2 to 2.9.3.

Release notes

Sourced from tensorflow-cpu's releases.

TensorFlow 2.9.3

Release 2.9.3

This release introduces several vulnerability fixes:

Fixes an overflow in tf.keras.losses.poisson (CVE-2022-41887)

Fixes a heap OOB failure in ThreadUnsafeUnigramCandidateSampler caused by missing validation (CVE-2022-41880)

Fixes a segfault in ndarray_tensor_bridge (CVE-2022-41884)

Fixes an overflow in FusedResizeAndPadConv2D (CVE-2022-41885)

Fixes a overflow in ImageProjectiveTransformV2 (CVE-2022-41886)

Fixes an FPE in tf.image.generate_bounding_box_proposals on GPU (CVE-2022-41888)

Fixes a segfault in pywrap_tfe_src caused by invalid attributes (CVE-2022-41889)

Fixes a CHECK fail in BCast (CVE-2022-41890)

Fixes a segfault in TensorListConcat (CVE-2022-41891)

Fixes a CHECK_EQ fail in TensorListResize (CVE-2022-41893)

Fixes an overflow in CONV_3D_TRANSPOSE on TFLite (CVE-2022-41894)

Fixes a heap OOB in MirrorPadGrad (CVE-2022-41895)

Fixes a crash in Mfcc (CVE-2022-41896)

Fixes a heap OOB in FractionalMaxPoolGrad (CVE-2022-41897)

Fixes a CHECK fail in SparseFillEmptyRowsGrad (CVE-2022-41898)

Fixes a CHECK fail in SdcaOptimizer (CVE-2022-41899)

Fixes a heap OOB in FractionalAvgPool and FractionalMaxPool(CVE-2022-41900)

Fixes a CHECK_EQ in SparseMatrixNNZ (CVE-2022-41901)

Fixes an OOB write in grappler (CVE-2022-41902)

Fixes a overflow in ResizeNearestNeighborGrad (CVE-2022-41907)

Fixes a CHECK fail in PyFunc (CVE-2022-41908)

Fixes a segfault in CompositeTensorVariantToComponents (CVE-2022-41909)

Fixes a invalid char to bool conversion in printing a tensor (CVE-2022-41911)

Fixes a heap overflow in QuantizeAndDequantizeV2 (CVE-2022-41910)

Fixes a CHECK failure in SobolSample via missing validation (CVE-2022-35935)

Fixes a CHECK fail in TensorListScatter and TensorListScatterV2 in eager mode (CVE-2022-35935)

TensorFlow 2.9.2

Release 2.9.2

This releases introduces several vulnerability fixes:

Fixes a CHECK failure in tf.reshape caused by overflows (CVE-2022-35934)

Fixes a CHECK failure in SobolSample caused by missing validation (CVE-2022-35935)

Fixes an OOB read in Gather_nd op in TF Lite (CVE-2022-35937)

Fixes a CHECK failure in TensorListReserve caused by missing validation (CVE-2022-35960)

Fixes an OOB write in Scatter_nd op in TF Lite (CVE-2022-35939)

Fixes an integer overflow in RaggedRangeOp (CVE-2022-35940)

Fixes a CHECK failure in AvgPoolOp (CVE-2022-35941)

Fixes a CHECK failures in UnbatchGradOp (CVE-2022-35952)

Fixes a segfault TFLite converter on per-channel quantized transposed convolutions (CVE-2022-36027)

Fixes a CHECK failures in AvgPool3DGrad (CVE-2022-35959)

Fixes a CHECK failures in FractionalAvgPoolGrad (CVE-2022-35963)

Fixes a segfault in BlockLSTMGradV2 (CVE-2022-35964)

Fixes a segfault in LowerBound and UpperBound (CVE-2022-35965)

... (truncated)

Changelog

Sourced from tensorflow-cpu's changelog.

Release 2.9.3

This release introduces several vulnerability fixes:

Fixes an overflow in tf.keras.losses.poisson (CVE-2022-41887)

Fixes a heap OOB failure in ThreadUnsafeUnigramCandidateSampler caused by missing validation (CVE-2022-41880)

Fixes a segfault in ndarray_tensor_bridge (CVE-2022-41884)

Fixes an overflow in FusedResizeAndPadConv2D (CVE-2022-41885)

Fixes a overflow in ImageProjectiveTransformV2 (CVE-2022-41886)

Fixes an FPE in tf.image.generate_bounding_box_proposals on GPU (CVE-2022-41888)

Fixes a segfault in pywrap_tfe_src caused by invalid attributes (CVE-2022-41889)

Fixes a CHECK fail in BCast (CVE-2022-41890)

Fixes a segfault in TensorListConcat (CVE-2022-41891)

Fixes a CHECK_EQ fail in TensorListResize (CVE-2022-41893)

Fixes an overflow in CONV_3D_TRANSPOSE on TFLite (CVE-2022-41894)

Fixes a heap OOB in MirrorPadGrad (CVE-2022-41895)

Fixes a crash in Mfcc (CVE-2022-41896)

Fixes a heap OOB in FractionalMaxPoolGrad (CVE-2022-41897)

Fixes a CHECK fail in SparseFillEmptyRowsGrad (CVE-2022-41898)

Fixes a CHECK fail in SdcaOptimizer (CVE-2022-41899)

Fixes a heap OOB in FractionalAvgPool and FractionalMaxPool(CVE-2022-41900)

Fixes a CHECK_EQ in SparseMatrixNNZ (CVE-2022-41901)

Fixes an OOB write in grappler (CVE-2022-41902)

Fixes a overflow in ResizeNearestNeighborGrad (CVE-2022-41907)

Fixes a CHECK fail in PyFunc (CVE-2022-41908)

Fixes a segfault in CompositeTensorVariantToComponents (CVE-2022-41909)

Fixes a invalid char to bool conversion in printing a tensor (CVE-2022-41911)

Fixes a heap overflow in QuantizeAndDequantizeV2 (CVE-2022-41910)

Fixes a CHECK failure in SobolSample via missing validation (CVE-2022-35935)

Fixes a CHECK fail in TensorListScatter and TensorListScatterV2 in eager mode (CVE-2022-35935)

Release 2.8.4

This release introduces several vulnerability fixes:

Fixes a heap OOB failure in ThreadUnsafeUnigramCandidateSampler caused by missing validation (CVE-2022-41880)

Fixes a segfault in ndarray_tensor_bridge (CVE-2022-41884)

Fixes an overflow in FusedResizeAndPadConv2D (CVE-2022-41885)

Fixes a overflow in ImageProjectiveTransformV2 (CVE-2022-41886)

Fixes an FPE in tf.image.generate_bounding_box_proposals on GPU (CVE-2022-41888)

Fixes a segfault in pywrap_tfe_src caused by invalid attributes (CVE-2022-41889)

Fixes a CHECK fail in BCast (CVE-2022-41890)

Fixes a segfault in TensorListConcat (CVE-2022-41891)

Fixes a CHECK_EQ fail in TensorListResize (CVE-2022-41893)

Fixes an overflow in CONV_3D_TRANSPOSE on TFLite (CVE-2022-41894)

Fixes a heap OOB in MirrorPadGrad (CVE-2022-41895)

Fixes a crash in Mfcc (CVE-2022-41896)

Fixes a heap OOB in FractionalMaxPoolGrad (CVE-2022-41897)

Fixes a CHECK fail in SparseFillEmptyRowsGrad (CVE-2022-41898)

Fixes a CHECK fail in SdcaOptimizer (CVE-2022-41899)

... (truncated)

Commits

a5ed5f3 Merge pull request #58584 from tensorflow/vinila21-patch-2

258f9a1 Update py_func.cc

cd27cfb Merge pull request #58580 from tensorflow-jenkins/version-numbers-2.9.3-24474

3e75385 Update version numbers to 2.9.3

bc72c39 Merge pull request #58482 from tensorflow-jenkins/relnotes-2.9.3-25695

3506c90 Update RELEASE.md

8dcb48e Update RELEASE.md

4f34ec8 Merge pull request #58576 from pak-laura/c2.99f03a9d3bafe902c1e6beb105b2f2417...

6fc67e4 Replace CHECK with returning an InternalError on failing to create python tuple

5dbe90a Merge pull request #58570 from tensorflow/r2.9-7b174a0f2e4

Additional commits viewable in compare view

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Dependabot commands and options

You can trigger Dependabot actions by commenting on this PR:

@dependabot rebase will rebase this PR

@dependabot recreate will recreate this PR, overwriting any edits that have been made to it

@dependabot merge will merge this PR after your CI passes on it

@dependabot squash and merge will squash and merge this PR after your CI passes on it

@dependabot cancel merge will cancel a previously requested merge and block automerging

@dependabot reopen will reopen this PR if it is closed

@dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually

@dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)

@dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)

@dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)

@dependabot use these labels will set the current labels as the default for future PRs for this repo and language

@dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language

@dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language

@dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

You can disable automated security fix PRs for this repo from the Security Alerts page.

PR: unreviewed dependencies
opened by dependabot[bot] 0
Bump python-jwt from 3.2.4 to 3.3.4
Bumps python-jwt from 3.2.4 to 3.3.4.

Commits

See full diff in compare view

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Dependabot commands and options

You can trigger Dependabot actions by commenting on this PR:

@dependabot rebase will rebase this PR

@dependabot recreate will recreate this PR, overwriting any edits that have been made to it

@dependabot merge will merge this PR after your CI passes on it

@dependabot squash and merge will squash and merge this PR after your CI passes on it

@dependabot cancel merge will cancel a previously requested merge and block automerging

@dependabot reopen will reopen this PR if it is closed

@dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually

@dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)

@dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)

@dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)

@dependabot use these labels will set the current labels as the default for future PRs for this repo and language

@dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language

@dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language

@dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

You can disable automated security fix PRs for this repo from the Security Alerts page.

PR: unreviewed dependencies
opened by dependabot[bot] 0
Breast Cancer Prediction

I would like to work on the problem of predicting the most common form of breast cancer (Invasive Ductal Carcinoma (IDC)). For this, I'll use breast histopathology images data.

opened by SoniGarima 0

An open-source project for applying deep learning to medical scenarios

Related tags

Overview

Auto Vaidya

Suggestions for PR:

Comments

Adding Feedback Page

9.3.0

Changes

9.3.0 (2022-10-29)

TensorFlow 2.9.3

Release 2.9.3

TensorFlow 2.9.2

Release 2.9.2

Release 2.9.3

Release 2.8.4

Owner

Smaranjit Ghose

The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.

Build a medical knowledge graph based on Unified Language Medical System (UMLS)

MetaDrive: Composing Diverse Scenarios for Generalizable Reinforcement Learning

Breaching - Breaching privacy in federated learning scenarios for vision and text

TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-Captured Scenarios

CRISCE: Automatically Generating Critical Driving Scenarios From Car Accident Sketches

Image morphing without reference points by applying warp maps and optimizing over them.

Face2webtoon - Despite its importance, there are few previous works applying I2I translation to webtoon.

SparseML is a libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

Fuzzification helps developers protect the released, binary-only software from attackers who are capable of applying state-of-the-art fuzzing techniques

PaddleRobotics is an open-source algorithm library for robots based on Paddle, including open-source parts such as human-robot interaction, complex motion control, environment perception, SLAM positioning, and navigation.

Project Aquarium is a SUSE-sponsored open source project aiming at becoming an easy to use, rock solid storage appliance based on Ceph.

ivadomed is an integrated framework for medical image analysis with deep learning.

TorchIO is a Medical image preprocessing and augmentation toolkit for deep learning. Part of the PyTorch Ecosystem.

[MedIA2021]MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning

A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation

Useful materials and tutorials for 110-1 NTU DBME5028 (Application of Deep Learning in Medical Imaging)

Medical image analysis framework merging ANTsPy and deep learning