Biomedical Image Processing, such as human organ segmentation and disease analysis, is a modern ﬁeld in medicine development and patient treatment. Besides there are many kinds of image formats, the diversity and complexity of biomedical data is still a big issue to all of researchers in their applications. In order to deal with the problem, deep learning give us a successful and effective solutions. Unet and LSTM are two general approaches to the most of case of medical image data. While Unet helps to teach a machine in learning data from each image accompanied with its labelled information, LSTM helps to remember states from many slices of images by times. Unet gives us the segmentation of tumor, abnormal things from biomedical images and then the LSTM gives us the effective diagnosis on a patient disease. In this paper, we show some scenarios of using Unets and LSTM to segment and analysis on many kinds of human organ images and results of brain, retinal, skin, lung and breast segmentation.
Savelie Cornegruta, Robert Bakewell, Samuel Withey, and Giovanni Montana. Modeling radiological language with bidirectional long short-term memory networks. CoRR, abs/1609.08409, 2016.
Geert J. S. Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian,JeroenA.W.M.vanderLaak,BramvanGinneken, and Clara I. Sánchez. A survey on deep learning in medical image analysis. CoRR, abs/1702.05747, 2017.
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. CoRR, abs/1505.04597, 2015.
Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9:1735–80, 12 1997.
Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully convolutional networks for semantic segmentation. CoRR, abs/1411.4038, 2014.
T. Falk, D. Mai, R. Bensch, Ö. Çiçek, A. Abdulkadir, Y. Marrakchi, A. Böhm, J. Deubner, Z. Jäckel, K. Seiwald, A. Dovzhenko, O. Tietz, C. Dal Bosco, S. Walsh, D. Saltukoglu, T. L. Tay, M. Prinz, K. Palme, M. Simons, I. Diester, T. Brox, and O. Ronneberger. U-net – deep learning for cell counting, detection, and morphometry. Nature Methods, 16:67–70, 2019.
F.A.Gers, J.Schmidhuber, and F.Cummins. Learning to forget: continual prediction with lstm. In 1999 Ninth International Conference on Artiﬁcial Neural Networks ICANN 99. (Conf. Publ. No. 470), volume 2, pages 850–855 vol.2, 1999.
VarunGulshan, LilyPeng, MarcCoram, MartinStumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros, Ramasamy Kim, Rajiv Raman, Philip Nelson, Jessica Mega, and Dale Webster. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.JAMA, 316, 11 2016.
Y. Bar, I. Diamant, L. Wolf, S. Lieberman, E. Konen, andH. Greenspan. Chest pathology detection using deep learning with non-medical training. In2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pages 294–297, 2015