1. A Neural Network Approach for classifying TACS By Mike Smith

2. Personal Background ECE Master’s student Research Assistant for the Laboratory for Optical and Computational Instrumentation (LOCI) –Design Control System for Laser Scanning Microscopes

3. Project Overview Classifying TACS (Tumor Associated Collagen Signatures) –Change in density and alignment during tumor development –Signatures can be seen before a tumor is formed allowing for early detection of cancer

4. Data Gathering Data is gathered using multiphoton laser scanning microscope Collagen produces a second harmonic effect naturally –Basically shine a laser on collagen, it will glow and we can capture that and form an image

5. Training/Testing Data -Classified data from images of tumors from a mouse mammary -Broken up into 32x32 discrete chunks

6. Current Techniques Classify intensity or average intensity of a section of data using artificial neural networks –Naive approach –Haven’t been happy with results –Gives baseline though Classify based on change in intensity and consistency with areas around it

7. Future Work Classify based on raw data, not images –Only 8 bit pixels, ADC provides 12 bit resolution Try to predict signatures before tumor is formed -Early Cancer Detection

8. References P. P. Provenzano, et al., "Collagen reorganization at the tumor-stromal interface facilitates local invasion". BMC Med. 4, 38 (2006). www.loci.wisc.edu