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Publications

Publications by CTM

2024

Deep Learning-based Prediction of Breast Cancer Tumor and Immune Phenotypes from Histopathology

Authors
Gonçalves, T; Arias, DP; Willett, J; Hoebel, KV; Cleveland, MC; Ahmed, SR; Gerstner, ER; Cramer, JK; Cardoso, JS; Bridge, CP; Kim, AE;

Publication
CoRR

Abstract

2024

Explainable Deep Learning Methods in Medical Image Classification: A Survey

Authors
Patrício, C; Neves, C; Teixeira, F;

Publication
ACM COMPUTING SURVEYS

Abstract
The remarkable success of deep learning has prompted interest in its application to medical imaging diagnosis. Even though 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 nature 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 imaging diagnosis, including visual, textual, example-based and concept-based explanation methods. Moreover, this work reviews the existing medical imaging datasets and the existing metrics for evaluating the quality of the explanations. In addition, we include a performance comparison among a set of report generation-based methods. Finally, the major challenges in applying XAI to medical imaging and the future research directions on the topic are discussed.

2024

Towards Concept-Based Interpretability of Skin Lesion Diagnosis Using Vision-Language Models

Authors
Patrício, C; Teixeira, LF; Neves, JC;

Publication
IEEE International Symposium on Biomedical Imaging, ISBI 2024, Athens, Greece, May 27-30, 2024

Abstract
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. © 2024 IEEE.

2024

Multimodal PointPillars for Efficient Object Detection in Autonomous Vehicles

Authors
Oliveira, M; Cerqueira, R; Pinto, JR; Fonseca, J; Teixeira, LF;

Publication
IEEE Transactions on Intelligent Vehicles

Abstract

2024

Finding Patterns in Ambiguity: Interpretable Stress Testing in the Decision~Boundary

Authors
Gomes, I; Teixeira, LF; van Rijn, JN; Soares, C; Restivo, A; Cunha, L; Santos, M;

Publication
CoRR

Abstract

2024

Markerless Multi-view 3D Human Pose Estimation: a survey

Authors
Rodrigues Nogueira, AF; Oliveira, HP; Teixeira, LF;

Publication
CoRR

Abstract

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