How do you train a deep neural network to recognize images?

Balancing your dataset is as critical as its size and diversity. If you collect thousands of cat images but only a handful of dog images, your DNN might become biased toward recognizing cats. To achieve a well-rounded model, it's essential to ensure an equitable representation of each class. Moreover, always be on the lookout for potential data collection biases, as these can inadvertently influence your DNN's predictions and overall reliability

Sometimes if not many diverse images are readily available but we wish to do the modelling, in such a scenario, we go for Data Augmentation. It is a technique wherein we artificially increase the training set by creating modified copies of a dataset using existing data. It includes making minor changes to the dataset or using deep learning to generate new data points. Some of the examples of Position augmentation are: Scaling, Cropping, Flipping, Padding, Rotation, Translation, Affine transformation. Similarly, some of the examples for Color augmentation are: Brightness, Contrast, Saturation, and Hue.

Data collection is the foundational step in training deep neural networks. In the realm of wound care, the quality and diversity of the collected data are paramount. Accurate recognition and analysis of various wound types, stages, and complications rely on a comprehensive dataset. By gathering diverse wound images from different demographics, environments, and lighting conditions, AI models can be fine-tuned to offer more precise diagnoses and treatment recommendations. Ensuring this diversity can lead to more inclusive and effective AI-driven solutions in wound care.

If you are going to train images, you should definitely use CNN. You cannot connect images directly to the fully connected layer. After obtaining the features with CNN's, fully connected layers can be added to the last layers to classify them. Also, don't forget to put batch normalization and pooling layers between CNN layers.

Additionally, to classic approaches, Synthetic Data proves to be a valuable option to quickly add variability, volume, and balance in data. It accelerates object recognition by replacing the need for extensive, time-consuming manual labeling. With synthetic computer vision, we create 3D models of objects, place them in a virtual photorealistic environment that mirrors reality, and generate as many images as necessary from different angles, lighting conditions, and cameras+lens combinations. Data annotation is performed automatically, with 100% accuracy. Offering unique advantages for the performance of machine learning models. In our experience, this is especially useful for computer vision in retail with ever-changing product packaging.

Data preprocessing is a critical step that ensures the data fed into neural networks is clean, relevant, and structured. For wound care, this involves refining wound images, standardizing resolution, enhancing contrast, or eliminating background noise. By doing so, clinicians can trust that AI-driven analyses are based on the most salient features of the wound, without interference from extraneous data. A meticulous preprocessing routine enhances AI's capability to provide consistent and reliable insights, ensuring that care providers receive the most accurate information to inform their treatment decisions.

Image preprocessing is a vital step to improve the quality of image dataset which enhances the speed of analysis and processing. Contrast enhancement, noise reduction, color correction, image resizing, segmentation, and feature extraction are a few examples of image preprocessing techniques.

When dealing with unstructured data like images & videos for building vision-based models, data preprocessing is an important step. This can be done using core image processing techniques with the help of OpenCV, Pillow (PIL: Python Imaging Library), scikit-image, scikit-video, and more. This is to ensure that the correct data goes to the model building to derive better outcomes.

La calidad de la imágenes es una las características que incide directamente, en la etapa del Preprocesamiento de datos para una DNN. La tecnología con la que generas la imagen fuente puede ayudarte a que el tiempo de preprocesamiento se acorte. Hoy en día ya existe tecnología muy poderosa que permite hacer esta etapa con una mayor velocidad. Hace algunos a?os esta etapa era la que consumía mas tiempo en el proceso de entrenamiento. OpenCV es una biblioteca muy importante para realizar esta etapa. Sin dejar de lado a la potencia que TensorFlow nos brinda en la actualidad

One important thing to note is that augmentations is done for many reasons such as increasing dataset size, making the model robust to a different kind of images belonging to a certain class, or regularization. However, it's important to exercise caution and avoid using strong augmentation, as this could potentially hinder the process of acquiring meaningful representations.

Balancing model complexity with computational efficiency is crucial. While deeper and more intricate models can, in theory, capture intricate patterns in the data, they also demand more computational power and are prone to overfitting, especially with limited data. In many real-world applications I've worked on, simpler models with proper regularization not only trained faster but also generalized better on unseen data

R-CNN, Fast R-CNN, Faster R-CNN, YOLO model family are a few CNN models used for image classification and object detection. Certain transfer learning models such as AlexNet, Vgg19 can also be used for image classification. A few examples of hyper parameters for these models are: no. of dropout layers, kernel size, number of filters, number of layers, activation functions, number of classes, augmentation type, early stopping, optimizers, weights and biases.

Model design is at the heart of tailoring AI solutions to specific challenges. In the realm of wound care, this means creating neural network architectures optimized for analyzing complex wound characteristics. Such a design should account for the variety of wound types, stages, and complications. A well-structured model can discern subtle changes in wound progression, differentiate between various wound types, and even predict potential complications. As eKare and other entities continue to refine their AI models, clinicians can look forward to even more nuanced insights, driving personalized care plans and improving patient outcomes.

Model Design is a bit of an art and science. The choice of model design and architecture depends upon several factors, like data, latency, compute, and the problem we are solving. We could surely design a DNN from scratch, however, with Transfer learning and the Large Language model pertaining-finetuning paradigm has become the new defacto. For industrial use, however, we could look further into knowledge distillation techniques for deployment.

Designing custom architectures for each CV application either requires the conduction of massive retraining or implies losing pretraining benefits; thus not always is used in practice. On the other hand, slight adjustments to the architecture, like changing the number of input channels, can be made while retaining and fine-tuning almost all pre-trained weights. This approach allows for some customization without sacrificing the benefits of pretraining. In practice, the most frequently used pre-trained architectures are those that have proven themselves on benchmark datasets like ImageNet. These models serve as strong starting points, saving time and resources compared to designing entirely custom architectures.

Model training is where the AI system truly learns to make sense of the information it's given. In wound care, this step is pivotal. By feeding the model vast amounts of wound-related data – from images to patient histories – the AI becomes adept at recognizing and interpreting wound characteristics. This iterative process of learning from errors refines the model's accuracy. With platforms like eKare's, continuous model training can enhance predictive capabilities, enabling earlier interventions and more precise treatment paths. As more data is added, the AI becomes an even more valuable ally to clinicians in their quest for optimal patient care.

Early stopping for image classification or object detection can be defined as a regularization technique that actually can improve the model accuracy by stopping the training process when validation loss increases. Callbacks can be defined as pre-defined functions that can be called repeatedly during a process that can help in correcting certain model behaviours. SGD and Adam (adaptive moment estimation) are optimizers that might work well for binary image classification. Adagrad, RMSProp and Adam can work well for non-binary image classification.

The data used to train an AI model is the most important part of the process. If you are using a pre-trained model developed by XYZ company, the company retains ownership of the model. However, the data that you generate using the model belongs to you. If the generated data is politically inaccurate, the responsibility lies with the person who generated the data.

It’s important to understand the key hyperparameters involved in model training and how they exactly impact the computer vision problem you are solving. Generally, optimizer and learning rate should be the default ones, batch size and loss function are candidates to be tuned and the augmentation pipeline should be carefully designed task to task. A good practice would be to track a large range of evaluation factors during the training: visualizations, metrics, loss values, stability measurements, etc. The training process decisions should be based on the complete analysis of the evaluation factors, and custom computer vision quality assurance procedures.

Accuracy, precision, recall, F1-Score, ROC Curve, AUC score, Confusion matrix can be used as a few metrics for model evaluation in case of images. Accuracy can be symbolically described as a school MCQ test, you score high if you get most of them right, you score low if you get most of them wrong. Precision and recall have an inverse relationship. If precision is high, recall is low and vice versa. Precision can be calculated by taking the ratio of True Positives over All the positives (True and False). Recall can be calculated by taking the ratio of True Positives and the sum of True Positives and False negatives. For sensitive use cases such as detecting cancer requires high recall to ensure not to miss any cases where cancer is present.

The balance between overfitting and underfitting can be tricky. Strangely enough, it can be like oversteer and understeer in cars in that sometimes you get a weird combination of both. With non-IID or noisy data, even with a relatively small model you might get a result that looks like overfitting but also underfitting; in the sense that your model is too small to learn useful patterns but also the patterns that it does learn do not generalize well to unseen data.

Once, during my project, I was stuck with a low accuracy. Changes and improvements in model architecture were not resulting in a significant improvement in accuracy. Then I utilized these set of metrics and visualizations to understand the pain-points of my machine learning pipeline. This helped me tune for those specific subsections of dataset and algorithms.

Model evaluation is an area that it’s easy to skimp on, partly because it is quite challenging to do well However this step is critical to getting business value from all the hard work that goes into training a model. Think carefully about the most effective ways to evaluate your model performance against the real world situation you will be using it in and don’t get lost in statistical measures which you’ve seen used but you can’t clearly explain.

One aspect of model deployment that's frequently underestimated is the continuous monitoring and maintenance post-deployment. Once live, a model's performance can drift due to evolving data landscapes or changing user behaviors. Establishing a robust monitoring system that alerts you to significant performance changes is critical. This proactive approach not only ensures that the model remains effective but also minimizes potential disruptions to end-users

Great point in Model monitoring. For eg , Vertex AI from google cloud provide the following features to detect model performance in early stage to avoid poor performance of the model over the time. Training-serving skew Occurs when the feature data distribution in production changes from the distribution of data that is used to train the model. Prediction drift happens when feature data distribution in production changes significantly over time. If the original training data. You can enable this feature and monitor your model performance and act quickly before it provides inaccurate results or serving.

Training a neural network to recognize images, few additional points should be consider: 1. Right amount of data (volume) 2. Varied scenarios of data (variety) 3. Accurate labelling of data to identify/detect/classify within the image (data annotation) 4. Applying the right set of data pre-processing techniques to transform/process images. 5. Applying the right set of data augmentation techniques (if needed) 6. Choosing the right algorithm (depending on overall model metrics & business goals) 7. Choosing the right deployment mechanism (whether cloud deployment or edge deployment for real-time inferencing).

Responsible AI and ethical use of data in the model training is of utmost importance. AI specialists must stop and think about our model predictions' implications and consequences.

Balancing innovation with privacy is pivotal in image recognition. As Information Security Officer and former DPO, I emphasize: 1. Anonymization: Essential when collecting identifiable images. Pseudonymization can make data non-identifiable. 2. Informed Consent: Ensure individuals are aware of the data's intended use before collecting images. 2. Access and Portability: With GDPR, people can access and transfer their images. Provide means for this. 3. Secure Storage: Images must be stored securely with clear retention and destruction policies. 4. Risk Assessments: Regularly assess risks associated with images to address vulnerabilities. Conclusion: Data protection in image recognition is a must to use technology ethically.

The world of images is vast and ever-changing (especially if we are dealing with objects/products). A model trained today might not be as effective in a year or two. It's essential to have a mechanism where the DNN can continuously learn from new data, adapt to changing scenarios, and even unlearn biases it might have acquired.