The SmartCat team addressed the problem by implementing a machine learning model using image processing techniques and deep learning algorithms.

1. Preprocessing: The team converted the video files into a set of images (frames) using OpenCV. Various transformations were applied to extract embryos, and data augmentation techniques were used to increase the training set.
2. Dimensionality Reduction (Embedding): The team employed unsupervised learning techniques, specifically Variational Autoencoder and Stacked Denoising Autoencoder, to extract features from the image frames and create a lower-dimensional representation as vector embeddings.
3. Video Classification: The embeddings were used as sequences to make final predictions for the video. Support Vector Machine (SVM) was used for single image classification, and Long Short Term Memory (LSTM) was used for whole video classification.

Results

The approach achieved solid results in classifying images and videos on the test set. The next step for the team is to extend the model with a larger video dataset and combine results from images and metadata to improve accuracy further.

In similar situations, it is important to consider using unsupervised learning techniques for dimensionality reduction and feature extraction, especially when dealing with video data. Data augmentation can also help expand the training set and improve model performance.

Embryo classification using image processing and deep learning techniques can significantly assist healthcare professionals in making informed decisions about successful pregnancies, potentially improving the outcomes of human reproduction procedures.