How do you train and update your image classification model with new data?

In my domain of histopathology, I focus not on angles and shadows, but on tissue staining/coloring. Acquiring architecturally salient structures in small FOVs that also capture variety of cell types. This also achieves a sort of microdissection overlay over macro dissection. Later segmentation is then more useful and gives better results when tested.

It's not just about having large volumes of data; the quality, relevance, and diversity of that data are equally important. In my experience, diverse datasets that capture variations in conditions—such as lighting, angles, and backgrounds—are essential for building robust models that perform well in real-world applications. Equally important is consistent and accurate labeling. Using a clear, specific naming convention ensures that your model understands the differences between classes, reducing confusion and improving overall performance. Poor labeling can lead to misclassification and poor results, no matter how advanced the model architecture.

My work is working with videos requires a careful approach when selecting a data source for machine learning. Relevance, diversity, and a substantial volume of accurately labeled video frames are vital for real-world applicability and effective training, especially for deep-learning models. Ethical considerations, such as privacy, must be prioritized, and comprehensive documentation of sources and methods is essential. Additionally, anticipating scalability and embracing the iterative nature of data collection are key strategies for developing a robust and adaptable machine-learning model in the realm of video analysis.

Select data sources that provide images relevant to your classification, review the data to make sure it is robust enough, offering images in terms of lighting, angles, backgrounds, and contexts to improve model generalization. You could use publicly available datasets like ImageNet, COCO, or CIFAR for general-purpose tasks, or domain-specific repositories like Kaggle, medical imaging databases, or industry-specific datasets. Platforms like Amazon Mechanical Turk for custom data collection could also be a good crowd-sourced database. Ensure the data sources comply with privacy laws and ethical standards. Obtain necessary permissions and respect copyright laws.

I agree with the suggestion from Brian Cone about testing on real world data since vision models are really good at learning about the distribution of the input data (images) and using test data from the same distribution may not be as representative about the model's performance as compared to using test data from the real world, which may be slightly out of distribution.

Agree with the terms and hope we all use them similarly. Only thing I do differently is, given the how readily available histopathology “datasets” are, I do 80/20/0 80% train 20% validation 0% saved for testing. -Rather, I then extract new/unseen whole slide images from numerous open source and academic pathology archives. Test on the real world.

In my experience splitting the data into training and validation sets is a standard practice in machine learning. The training set is utilized to train your model, while the validation set is instrumental in tuning model parameters and evaluating its performance. With unique approach involves employing real-world data exclusively for testing, providing a robust assessment of how model's accuracy on unseen scenarios. While the common split ratio is 80% for training and 20% for validation, these percentages can be adjusted based on your specific data size and distribution, ensuring a tailored and effective model development process.

Choosing the right model architecture and framework is vital in the process. For image classification, options like ResNet18 and ResNet34 offer distinct approaches. Consideration of data characteristics, computational resources, and desired accuracy is crucial. Frameworks like TensorFlow, PyTorch, or Keras, aligned with your needs, provide tools for effective model development. The choice depends on aligning project requirements with the capabilities of the selected architecture and framework.

During the training and evaluation phase, essential parameters such as learning rate, epochs, batch size, dropout, early stopping, and loss function must be configured meticulously. Continuous monitoring of the model's performance on the validation set using metrics like accuracy, precision, recall, or F1-score is crucial for effective evaluation. Employing techniques such as data augmentation, regularization, and early stopping can play a significant role in enhancing the model's effectiveness and overall performance.

In a recent project, we employed Convolutional Neural Networks (CNN) using OpenCV and TensorFlow with NVIDIA GPU acceleration via CUDA to prevent system crashes. The model was trained on a carefully curated dataset with diverse, high-quality images. We utilized data augmentation to enhance generalization and applied transfer learning from pre-trained models like VGG16, ResNet, and InceptionV3. Evaluation metrics included accuracy, precision, recall, and F1-score, assessed through confusion matrices and ROC curves. This setup ensured robust and efficient model performance, leveraging advanced computational resources for optimal results.

You can use the models pre-trained on some large datasets in two different ways including: 1- feature extraction and feeding the extracted features to conventional classifiers such as SVM and Random Forest 2- freezing the layer excluding some few last layers of the pre-trained model and change the output layer architecture and activation function according to the target task and choosing the appropriate loss function, then feeding new data to the model and training the model. In this approach, the weights of the frozen layers will not be updated. These two approaches are called transfer learning and is very common and popular while we want to use a pre-trained model for a new dataset and maybe a different target task.

Updating your model with evolving data. Strategies like retraining, fine-tuning (adjusting parameters such as L1, L2 regularization or even using no regularization), and incremental learning are key. Retraining starts fresh, fine-tuning refines parameters, and incremental learning integrates new data without losing prior information. These strategies ensure your model stays accurate and adaptable, reflecting the changing dynamics of your data.

One of the most important aspects often overlooked in AI development is the need for continuous iteration and refinement. Even after deploying an AI model, it’s crucial to monitor its performance in real-world scenarios and regularly update it with new, diverse data. In my experience, no model is perfect right out of the gate, and its effectiveness can degrade over time as the data or environment changes. Also, collaboration between AI engineers and domain experts is key to success. In my view, the best AI solutions come from integrating technical expertise with deep domain knowledge to ensure that models are not only technically sound but also aligned with real-world needs.