A Two-Step Concept-Based Approach for Enhanced Interpretability and Trust in Skin Lesion Diagnosis

The main challenges hindering the adoption of deep learning-based systems in clinical settings are the scarcity of annotated data and the lack of interpretability and trust in these systems. Concept Bottleneck Models (CBMs) offer inherent interpretability by constraining the final disease prediction on a set of human-understandable concepts. However, this inherent interpretability comes at the cost of greater annotation burden. Additionally, adding new concepts requires retraining the entire system. In this work, we introduce a novel two-step methodology that addresses both of these challenges. By simulating the two stages of a CBM, we utilize a pretrained Vision Language Model (VLM) to automatically predict clinical concepts, and a Large Language Model (LLM) to generate disease diagnoses based on the predicted concepts. We validate our approach on three skin lesion datasets, demonstrating that it outperforms traditional CBMs and state-of-the-art explainable methods, all without requiring any training and utilizing only a few annotated examples. The code is available at https://github.com/CristianoPatricio/2-step-concept-based-skin-diagnosis.

Unsupervised Contrastive Analysis for Salient Pattern Detection using Conditional Diffusion Models

Contrastive Analysis (CA) regards the problem of identifying patterns in images that allow distinguishing between a background (BG) dataset (i.e. healthy subjects) and a target (TG) dataset (i.e. unhealthy subjects). Recent works on this topic rely on variational autoencoders (VAE) or contrastive learning strategies to learn the patterns that separate TG samples from BG samples in a supervised manner. However, the dependency on target (unhealthy) samples can be challenging in medical scenarios due to their limited availability. Also, the blurred reconstructions of VAEs lack utility and interpretability. In this work, we redefine the CA task by employing a self-supervised contrastive encoder to learn a latent representation encoding only common patterns from input images, using samples exclusively from the BG dataset during training, and approximating the distribution of the target patterns by leveraging data augmentation techniques. Subsequently, we exploit state-of-the-art generative methods, i.e. diffusion models, conditioned on the learned latent representation to produce a realistic (healthy) version of the input image encoding solely the common patterns. Thorough validation on a facial image dataset and experiments across three brain MRI datasets demonstrate that conditioning the generative process of state-of-the-art generative methods with the latent representation from our self-supervised contrastive encoder yields improvements in the generated image quality and in the accuracy of image classification. The code is available at https://github.com/CristianoPatricio/unsupervised-contrastive-cond-diff.

Towards Concept-based Interpretability of Skin Lesion Diagnosis using Vision-Language Models

Concept-based models naturally lend themselves to the development of inherently interpretable skin lesion diagnosis, as medical experts make decisions based on a set of visual patterns of the lesion. Nevertheless, the development of these models depends on the existence of concept-annotated datasets, whose availability is scarce due to the specialized knowledge and expertise required in the annotation process. In this work, we show that vision-language models can be used to alleviate the dependence on a large number of concept-annotated samples. In particular, we propose an embedding learning strategy to adapt CLIP to the downstream task of skin lesion classification using concept-based descriptions as textual embeddings. Our experiments reveal that vision-language models not only attain better accuracy when using concepts as textual embeddings, but also require a smaller number of concept-annotated samples to attain comparable performance to approaches specifically devised for automatic concept generation.

Coherent Concept-based Explanations in Medical Image and Its Application to Skin Lesion Diagnosis

Early detection of melanoma is crucial for preventing severe complications and increasing the chances of successful treatment. Existing deep learning approaches for melanoma skin lesion diagnosis are deemed black-box models, as they omit the rationale behind the model prediction, compromising the trustworthiness and acceptability of these diagnostic methods. Attempts to provide concept-based explanations are based on post-hoc approaches, which depend on an additional model to derive interpretations. In this paper, we propose an inherently interpretable framework to improve the interpretability of concept-based models by incorporating a hard attention mechanism and a coherence loss term to assure the visual coherence of concept activations by the concept encoder, without requiring the supervision of additional annotations. The proposed framework explains its decision in terms of human-interpretable concepts and their respective contribution to the final prediction, as well as a visual interpretation of the locations where the concept is present in the image. Experiments on skin image datasets demonstrate that our method outperforms existing black-box and concept-based models for skin lesion classification.

Explainable Deep Learning Methods in Medical Diagnosis: A Survey

The remarkable success of deep learning has prompted interest in its application to medical diagnosis. Even tough state-of-the-art deep learning models have achieved human-level accuracy on the classification of different types of medical data, these models are hardly adopted in clinical workflows, mainly due to their lack of interpretability. The black-box-ness of deep learning models has raised the need for devising strategies to explain the decision process of these models, leading to the creation of the topic of eXplainable Artificial Intelligence (XAI). In this context, we provide a thorough survey of XAI applied to medical diagnosis, including visual, textual, and example-based explanation methods. Moreover, this work reviews the existing medical imaging datasets and the existing metrics for evaluating the quality of the explanations . Complementary to most existing surveys, we include a performance comparison among a set of report generation-based methods. Finally, the major challenges in applying XAI to medical imaging are also discussed.

ZSpeedL -- Evaluating the Performance of Zero-Shot Learning Methods using Low-Power Devices

The recognition of unseen objects from a semantic representation or textual description, usually denoted as zero-shot learning, is more prone to be used in real-world scenarios when compared to traditional object recognition. Nevertheless, no work has evaluated the feasibility of deploying zero-shot learning approaches in these scenarios, particularly when using low-power devices. In this paper, we provide the first benchmark on the inference time of zero-shot learning, comprising an evaluation of state-of-the-art approaches regarding their speed/accuracy trade-off. An analysis to the processing time of the different phases of the ZSL inference stage reveals that visual feature extraction is the major bottleneck in this paradigm, but, we show that lightweight networks can dramatically reduce the overall inference time without reducing the accuracy obtained by the de facto ResNet101 architecture. Also, this benchmark evaluates how different ZSL approaches perform in low-power devices, and how the visual feature extraction phase could be optimized in this hardware. To foster the research and deployment of ZSL systems capable of operating in real-world scenarios, we release the evaluation framework used in this benchmark (https://github.com/CristianoPatricio/zsl-methods).