Deep learning has thrived by training on large-scale datasets. However, in many applications, as for medical image diagnosis, getting massive amount of data is still prohibitive due to privacy, lack of acquisition homogeneity and annotation cost. In this scenario, transfer learning from natural image collections is a standard practice that attempts to tackle shape, texture and color discrepancies all at once through pretrained model fine-tuning. In this work, we propose to disentangle those challenges and design a dedicated network module that focuses on color adaptation. We combine learning from scratch of the color module with transfer learning of different classification backbones, obtaining an end-to-end, easy-to-train architecture for diagnostic image recognition on X-ray images. Extensive experiments showed how our approach is particularly efficient in case of data scarcity and provides a new path for further transferring the learned color information across multiple medical datasets.

Bridging the gap between Natural and Medical Images through Deep Colorization / Morra, Lia; Piano, Luca; Lamberti, Fabrizio; Tommasi, Tatiana. - STAMPA. - (2021), pp. 835-842. ((Intervento presentato al convegno 25th International Conference on Pattern Recognition, ICPR 2020 tenutosi a Milano (Italy) nel 10-15 gennaio 2021 [10.1109/ICPR48806.2021.9412444].

Bridging the gap between Natural and Medical Images through Deep Colorization

Lia Morra;Luca Piano;Fabrizio Lamberti;Tatiana Tommasi
2021

Abstract

Deep learning has thrived by training on large-scale datasets. However, in many applications, as for medical image diagnosis, getting massive amount of data is still prohibitive due to privacy, lack of acquisition homogeneity and annotation cost. In this scenario, transfer learning from natural image collections is a standard practice that attempts to tackle shape, texture and color discrepancies all at once through pretrained model fine-tuning. In this work, we propose to disentangle those challenges and design a dedicated network module that focuses on color adaptation. We combine learning from scratch of the color module with transfer learning of different classification backbones, obtaining an end-to-end, easy-to-train architecture for diagnostic image recognition on X-ray images. Extensive experiments showed how our approach is particularly efficient in case of data scarcity and provides a new path for further transferring the learned color information across multiple medical datasets.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2837062