Siamese network for melanoma classification - 48515739

[MelMaillot]

This pull request implements a Siamese network for the classification of the ISIC 2020 Kaggle Challenge data set, a dataset of benign and malignant skin lesions.

Th code implements this functionality in python, using the Pytorch library. It introduces four files:

• dataset.py for data management (splitting augmentation and retrieval)
• modules.py for network architecture and loss functions (ResNet50 with classifier head, triplet loss)
• predict.py for model evaluation (Accuracy, ROC, AUPRC, t-SNE visualisation)
• train.py for training functions and main functionality (main function trains and tests a model according to hyperparameters specified in the file)

Please refer to the README file for more details on the functionality as well as a discussion on model results.

Thank you
Melissa Maillot - 48515739

[hanemma7moud]

This is an initial inspection, no action is required at this point

Recognition Problem : total : 19.5
Well Done !

1. Solves problem: The solution is appropriate for the problem (4.5)
   well-structured submission with strong medical-context discussion, extensive evaluation plots, and well-reasoned limitations. However, Embedding separation is weak and sensitvity is low. (-0.5)

2. Implementation functions : The code appears to be functional (3)

Good design: design is fairly strong(1)
Commenting: good(1)
Difficulty: Hard (10)

Overall Great work, thanks Melissa !

[gayanku]

Marking

Good/OK/Fair Practice (Design/Commenting, TF/Torch Usage)
	Adequate design and implementation.	-1
	Spacing and comments.
	Header blocks.
Recognition Problem
	OK solution to problem.	-1
	Driver Script present.
	File structure present.
	Good Usage & Demo & Visualisation & Data usage.
	Module present.
	Commenting present.
	No Data leakage found.
	Difficulty : Hard. Hard Difficulty : Siamese
Commit Log
	Good Meaningful commit messages.
	Good Progressive commits.
Documentation
	Readme :Good.
	Model/technical explanation :Good.
	Description and Comments :Good.
	Markdown used and PDF submitted.
Pull Request
	Successful Pull Request (Working Algorithm Delivered on Time in Correct Branch).
	No Feedback required.
	Request Description is good.
TOTAL		-2

Marked as per the due date and changes after which aren't necessarily allowed to contribute to grade for fairness.
Subject to approval from Shakes