A day full of new things to learn! They’re going to help us a whole lot in the next two weeks!
And now we’re gettin… https://t.co/kCjMQ1L290
5 hours, 46 minutes ago
A typical @WebValley day2 of work is when half of our high school colleagues already know more about topic than the… https://t.co/d2Ltar6pIv
7 hours, 16 minutes ago
This morning, @furlanello exlained us the details of the project that we are going to develop. We are ready to begi… https://t.co/pSToSon25s
7 hours, 42 minutes ago
Here we provide a list of the most useful papers to read before starting the WebValley 2018 School.
| # | Year | Authors | Title | Link |
|---|---|---|---|---|
| 1 | 1995 | David A Lisle | Introduction to Medical Imaging | Download |
| 2 | 2017 | Mohme M and Maire C and Riecken K et al. | Optical Barcoding for Single-Clone Tracking to Study Tumor Heterogeneity | Download |
| 3 | 2017 | H.K. van der Burgh et al. | Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis | Download |
| 4 | 2017 | Mohan et al. | MRI based medical image analysis: Survey on brain tumor grade classification | Download |
| 5 | 2017 | Vallières et al. | Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer | Download |
| 6 | 2017 | Miotto, R and Wang, F et al. | Deep learning for healthcare: review, opportunities and challenges | Download |
| 7 | 2017 | Litjens et al. | A Survey on Deep Learning in Medical Image Analysis | Download |
| 8 | 2017 | Mobadersany et al. | Predicting cancer outcomes from histology and genomics using convolutional networks | Download |
| 9 | 2017 | Ciompi F. et al. | Towards automatic pulmonary nodule management in lung cancer screening with deep learning | Download |
| 10 | 2017 | Garcia et al. | A Review on Deep Learning Techniques Applied to Semantic Segmentation | Download |
| 11 | 2017 | Liao et al. | Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network | Download |
| 12 | 2018 | Zhou et al. | Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches | Download |