## 3 Results and Discussions  
### Data Set Building  
An experiment on _ex vivo_ human adipose tissue specimens was conducted to test our method. To build our data set for deep learning method, we studied the histological photographs of visceral adipose tissue samples collected from 28 human subjects (12 diabetic subjects and 16 nondiabetic subjects) and calculated the accurate average size for each sample. Human subjects undergoing bariatric surgery were enrolled with Institutional Review Board approval from the University of Michigan and Ann Arbor Veterans Administration Hospital. Visceral adipose tissue (VAT) from the greater quantum was collected at the beginning of operation and processed immediately. By cutting collected tissues into small specimens we obtained 110 specimens of adipose. Then each specimen was divided into two parts which were used to for microscope analysis and to acquire PA signal, respectively. Adipose tissue explants were fixed in 10% formalin, embedded in paraffin, and sectioned onto charged glass slides. Fixed hematoxylin/eosin-stained slides were imaged on an microscope and analyzed with ImageJ software by a blinded observer, as previously described [1]. An example of histological photograph is shown in Figure 2c. For each histological photograph, pixel areas of all individual cells were averaged to evaluate the adipocyte size and the size distribution of the sample in Figure 2c and its Gamma fitting, which proved to be the best fit of adipocyte size distribution in previous research [17], are shown in Figure 2d. PA signal acquisition was conducted by using the setup shown in Figure 2a. Laser light was from a tunable Optical Parametric Oscillator pumped by the second harmonic output of an Nd:YAG pulsed laser. The laser illuminated the samples at the wavelength of 1210 nm where lipid has strong optical absorption [25]. During signal acquisition, the laser illuminated the whole adipose tissue specimen, achieving an averaged light fluence of 5.6 mJ/cm\({}^{2}\) on the sample surface. Each specimen was cut into an ellipsoid-like shape; sizes shown in Table 1. A needle hydrophone was set against the samples to acquire PA signals. Through a preamplifier and a low-noise amplifier, the PA signal received by the  
Figure 2: Experiment on _ex vivo_ human adipose tissue specimens. (**a**) Schematic diagram of experimental setup for PA signal acquisition. (**b**) Photograph of human adipose tissue specimens. (**c**) Representative microphotograph of a human adipose tissue sample demonstrating adipocyte microarchitecture. (**d**) Adipocyte size distribution corresponding to (**c**). hydrophobic was recorded with a digital oscilloscope. When recording PA signal, averaging was done by oscilloscope for 50 times for one PA signal. We listed the experiment parameters in Table 1.  
Considering the limitation of our sample size, we also performed data augmentation [26] to enlarge data set size. Before each training, we first randomly picked out 90% of the 110 samples (i.e., 99 samples) as a training set and the left (i.e., 11 samples) as a test set. Next, as shown in Figure 3a, the signal of each sample was divided into three parts that shared the same length and cover effective signal so that 297 samples and 33 samples were obtained for training set and test set respectively [27]. We also added white Gaussian noise to signals of training set and another two signals were obtained for each signal so that totally 891 samples were used for training. We assumed that the average adipocyte size for each signal was equal to the average adipocyte size of the signal which generated it. In our study, we performed this data augmentation each time before training. The power spectrum of each signal obtained through data augmentation was calculated. To reduce the effects of laser energy fluctuation on the signal, normalization was performed in the frequency