Approximate Inference for Determinantal Point Processes

In this thesis we explore a probabilistic model that is well-suited to a variety of subset selection tasks: the determinantal point process (DPP). DPPs were originally developed in the physics community to describe the repulsive interactions of fermions. More recently, they have been applied to machine learning problems such as search diversification and document summarization, which can be cast as subset selection tasks. A challenge, however, is scaling such DPP-based methods to the size of the datasets of interest to this community, and developing approximations for DPP inference tasks whose exact computation is prohibitively expensive. A DPP defines a probability distribution over all subsets of a ground set of items. Consider the inference tasks common to probabilistic models, which include normalizing, marginalizing, conditioning, sampling, estimating the mode, and maximizing likelihood. For DPPs, exactly computing the quantities necessary for the first four of these tasks requires time cubic in the number of items or features of the items. In this thesis, we propose a means of making these four tasks tractable even in the realm where the number of items and the number of features is large. Specifically, we analyze the impact of randomly projecting the features down to a lower-dimensional space and show that the variational distance between the resulting DPP and the original is bounded. In addition to expanding the circumstances in which these first four tasks are tractable, we also tackle the other two tasks, the first of which is known to be NP-hard (with no PTAS) and the second of which is conjectured to be NP-hard. For mode estimation, we build on submodular maximization techniques to develop an algorithm with a multiplicative approximation guarantee. For likelihood maximization, we exploit the generative process associated with DPP sampling to derive an expectation-maximization (EM) algorithm. We experimentally verify the practicality of all the techniques that we develop, testing them on applications such as news and research summarization, political candidate comparison, and product recommendation

Multimodal optical imaging platform for the early diagnosis for oral neoplasia

Early diagnosis is critical to reducing the global burden of oral cancer. In the US, 65% of oral cancer patients are diagnosed after regional metastasis; these patients have a 50% five-year mortality compared to 17% for those with localized disease. A major reason for late diagnosis is that clinicians are unable to accurately distinguish neoplastic lesions, which require treatment, from benign lesions. Furthermore, clinicians are unable to accurately select to biopsy the site with the worst diagnosis within a larger lesion. Please download the file below for full content

Rollerball microendoscope for mosaicking in high-resolution oral imaging

Only 40% of oral cancers are diagnosed at an early, localized stage, when treatment is most effective [1]. Thus, implementing diagnostic imaging tools for early detection of highgrade dysplasia and cancer may help improve the survival rate of oral cancer patients [2]. The highresolution microendoscope (HRME) is a compact, portable, fiberbased imaging device that can image cell nuclei in tissue labeled with the fluorescent contrast agent proflavine [3]. The HRME allows clinicians to noninvasively image the size, shape and distribution of epithelial cell nuclei in vivo, enabling realtime evaluation of potentially neoplastic lesions [3]. The primary limitation of the HRME is the small field of view of its fiber probe (720 μm), which makes it timeconsuming to examine large areas of tissue. Mosaicking algorithms have previously been implemented to allow realtime generation of image mosaics during HRME imaging, thus interrogating a larger field of view than the fiber probe’s diameter [4]. However, this approach has had limited success in vivo due to the practical difficulty of translating the fiber probe across the tissue in a smooth, controlled manner in order for the mosaicking software to function properly. Here we report the construction and initial testing of a rollerball HRME probe that permits smooth, rolling translation across the tissue surface while maintaining image quality with subcellular resolution. The rollerball HRME consists of a standard HRME probe interfaced with a rollerball mechanism. The mechanism is composed of two 5mm sapphire ball lenses enclosed within a 3D printed penlike casing. The ball lenses serve as an optical relay, while the distal ball lens also serves as a rolling contact point with the tissue surface. Figure 1 shows the use of the rollerball HRME to generate a realtime mosaic of a calibration target (field finder slide) as it rolls across the surface of the target. Figure 2 shows the use of the rollerball HRME to generate a realtime mosaic showing cell nuclei on the lateral tongue of a healthy volunteer as it rolls across the tissue surface. The rollerball HRME will allow clinicians to more rapidly examine large areas of tissue with subcellular resolution, potentially aiding in the early detection of highgrade oral dysplasia and cance. Please click Additional Files below to see the full abstract

Reconstructing Oral Cavity Tumor Evolution Through Brush Biopsy

Oral potentially malignant disorders (OPMDs) with genomic alterations have a heightened risk of evolving into oral squamous cell carcinoma (OSCC). Currently, genomic data are typically obtained through invasive tissue biopsy. However, brush biopsy is a non-invasive method that has been utilized for identifying dysplastic cells in OPMD but its effectiveness in reflecting the genomic landscape of OPMDs remains uncertain. This pilot study investigates the potential of brush biopsy samples in accurately reconstructing the genomic profile and tumor evolution in a patient with both OPMD and OSCC. We analyzed single nucleotide variants (SNVs), copy number aberrations (CNAs), and subclonal architectures in paired tissue and brush biopsy samples. The results showed that brush biopsy effectively captured 90% of SNVs and had similar CNA profiles as those seen in its paired tissue biopsies in all lesions. It was specific, as normal buccal mucosa did not share these genomic alterations. Interestingly, brush biopsy revealed shared SNVs and CNAs between the distinct OPMD and OSCC lesions from the same patient, indicating a common ancestral origin. Subclonal reconstruction confirmed this shared ancestry, followed by divergent evolution of the lesions. These findings highlight the potential of brush biopsies in accurately representing the genomic profile of OPL and OSCC, proving insight into reconstructing tumor evolution

Accuracy of In Vivo Multimodal Optical Imaging for Detection of Oral Neoplasia

If detected early, oral cancer is eminently curable. However, survival rates for oral cancer patients remain low, largely due to late-stage diagnosis and subsequent difficulty of treatment. To improve cliniciansﾒ ability to detect early disease and to treat advanced cancers, we developed a multimodal optical imaging system (MMIS) to evaluate tissue in situ, at macroscopic and microscopic scales. The MMIS was used to measure 100 anatomic sites in 30 patients, correctly classifying 98% of pathologically confirmed normal tissue sites, and 95% of sites graded as moderate dysplasia, severe dysplasia, or cancer. When used alone, MMIS classification accuracy was 35% for sites determined by pathology as mild dysplasia. However, MMIS measurements correlated with expression of candidate molecular markers in 87% of sites with mild dysplasia. These findings support the ability of noninvasive multimodal optical imaging to accurately identify neoplastic tissue and premalignant lesions. This in turn may have considerable impact on detection and treatment of patients with oral cancer and other epithelial malignancies