Historical Text Line Segmentation Using Deep Learning Algorithms: Mask-RCNN against U-Net Networks.

Text line segmentation is a necessary preliminary step before most text transcription algorithms are applied. The leading deep learning networks used in this context (ARU-Net, dhSegment, and Doc-UFCN) are based on the U-Net architecture. They are efficient, but fall under the same concept, requiring a post-processing step to perform instance (e.g., text line) segmentation. In the present work, we test the advantages of Mask-RCNN, which is designed to perform instance segmentation directly. This work is the first to directly compare Mask-RCNN- and U-Net-based networks on text segmentation of historical documents, showing the superiority of the former over the latter. Three studies were conducted, one comparing these networks on different historical databases, another comparing Mask-RCNN with Doc-UFCN on a private historical database, and a third comparing the handwritten text recognition (HTR) performance of the tested networks. The results showed that Mask-RCNN outperformed ARU-Net, dhSegment, and Doc-UFCN using relevant line segmentation metrics, that performance evaluation should not focus on the raw masks generated by the networks, that a light mask processing is an efficient and simple solution to improve evaluation, and that Mask-RCNN leads to better HTR performance.

Cancer therapy using nanoformulated substances: scientific, regulatory and financial aspects.

Several nanoformulated anti-cancer substances are currently commercialized or under development. Pre-clinical and clinical results have revealed better properties, that is, larger efficacy and lower toxicity for these substances than for conventional anti-cancer treatments. Here, we review the development of several of these substances such as Marqibo, Myocet, Doxil, DaunoXome, MM398, MM302, Mepact, Versamune, Thermodox, Depocyt, Livatag, Abraxane, Eligard, Opaxio, Zinostatin Stimalamer (SMANCS), Pegasys and PegIntron, BIND-014, CRLX-101, Oncaspar, Neulasta, Aurimmune, Auroshell, AuNPs, Nanotherm, NanoXray, Magnetosome chains, Kadcyla (T-DM1), Ontak (DAB/IL2), Gendicine and Curcumin. We describe their specific properties, such as their stability, solubility, mean of administration or targeting, distribution, metabolism and toxicity. We discuss their categorization as medical devices or drugs, their fabrication process within a regulatory environment as well as intellectual property and financial aspects that are all essential to enable their industrial development.