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

赦免

Mara Graziani,Niccolò Marini,Nicolas Deutschmann,Nikita Janakarajan,Henning Müller,María Rodríguez M

FID

prodrome

Paul Engstler,Matthias Keicher,David Schinz,Kristina Mach,Alexandra S. Gersing,Sarah C. Foreman,Soph

艱苦地移動

R. C. J. Kraaijveld,M. E. P. Philippens,W. S. C. Eppinga,I. M. Jürgenliemk-Schulz,K. G. A. Gilhuijs,

ARM

蕁麻

未開化

,Towards Self-explainable Transformers for?Cell Classification in?Flow Cytometry Data,classifies cells in FCM data by mimicking the decision process of medical experts. Our network considers all events of a sample at once and predicts the corresponding polygons of the gating hierarchy, thus, producing a verifiable visualization in the same way a human operator does. The proposed mode

合法

,Reducing Annotation Need in?Self-explanatory Models for?Lung Nodule Diagnosis,ttributes. Visualisation of the learned space further indicates that the correlation between the clustering of malignancy and nodule attributes coincides with clinical knowledge. Our complete code is open-source available: ..

驚呼

WITH

Beyond Voxel Prediction Uncertainty: Identifying Brain Lesions You Can Trust,e estimators of voxel uncertainty: entropy, variance, and model’s confidence; and can be applied to any lesion, regardless of its shape or size. We demonstrate the superiority of our approach for uncertainty estimate on a task of Multiple Sclerosis lesions segmentation.