paleolithic
发表于 2025-3-26 21:52:33
,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
Shuttle
发表于 2025-3-27 03:16:42
,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
匍匐
发表于 2025-3-27 06:20:15
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exorbitant
发表于 2025-3-27 12:05:30
,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
defendant
发表于 2025-3-27 16:05:07
,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
triptans
发表于 2025-3-27 17:49:32
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画布
发表于 2025-3-28 00:37:42
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违法事实
发表于 2025-3-28 03:56:18
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危险
发表于 2025-3-28 09:13:27
,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
Calculus
发表于 2025-3-28 13:39:32
,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