Titlebook: Interpretability of Machine Intelligence in Medical Image Computing; 5th International Wo Mauricio Reyes,Pedro Henriques Abreu,Jaime Cardos

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,Interpretable Lung Cancer Diagnosis with?Nodule Attribute Guidance and?Online Model Debugging,ly-used unsure nodule data such as LIDC-IDRI, we constructed a sure nodule data with gold-standard clinical diagnosis. To make the traditional CNN networks interpretable, we propose herewith a novel collaborative model to improve the trustworthiness of lung cancer predictions by self-regulation, whi

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,Do Pre-processing and?Augmentation Help Explainability? A?Multi-seed Analysis for?Brain Age Estimatnd efficient deep learning algorithms. There are two concerns with these algorithms, however: they are black-box models, and they can suffer from over-fitting to the training data due to their high capacity. Explainability for visualizing relevant structures aims to address the first issue, whereas

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,Reducing Annotation Need in?Self-explanatory Models for?Lung Nodule Diagnosis,semantic matching of clinical knowledge adds significantly to the trustworthiness of the AI. However, the cost of additional annotation of features remains a pressing issue. We address this problem by proposing cRedAnno, a data-/annotation-efficient self-explanatory approach for lung nodule diagnosi

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,Attention-Based Interpretable Regression of?Gene Expression in?Histology,mmendations. For models exceeding human performance, e.g. predicting RNA structure from microscopy images, interpretable modelling can be further used to uncover highly non-trivial patterns which are otherwise imperceptible to the human eye. We show that interpretability can reveal connections betwe

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,KAM - A Kernel Attention Module for?Emotion Classification with?EEG Data,es a self-attention mechanism by performing a kernel trick, demanding significantly fewer trainable parameters and computations than standard attention modules. The design also provides a scalar for quantitatively examining the amount of attention assigned during deep feature refinement, hence help

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,Explainable Artificial Intelligence for?Breast Tumour Classification: Helpful or?Harmful,hey make their decisions. For example, image explanations show us which pixels or segments were deemed most important by a model for a particular classification decision. This research focuses on image explanations generated by LIME, RISE and SHAP for a model which classifies breast mammograms as ei