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We propose a framework that leverages deep residual CNNs pretrained on large, non-biomedical image data sets. These pretrained networks learn cross-domain features that improve low-level interpretation of images. We evaluate our model on brain imaging data and show that pretraining and the use of deep residual networks are crucial to seeing large improvements in Alzheimer's Disease diagnosis from brain MRIs.

Parkinson's, a progressive neural disorder, is difficult to identify due to the hidden nature of the symptoms associated. We present a machine learning approach that uses a definite set of features obtained from the Parkinsons Progression Markers Initiative(PPMI) study as input and classifies them into one of two classes: PD(Parkinson's disease) and HC(Healthy Control). As far as we know this is the first work in applying machine learning algorithms for classifying patients with Parkinson's disease with the involvement of domain expert during the feature selection process. We evaluate our approach on 1194 patients acquired from Parkinsons Progression Markers Initiative and show that it achieves a state-of-the-art performance with minimal feature engineering.

With the advent of inexpensive microarray technology, biologists have become increasingly reliant on gene expression analysis for detecting disease states, including diagnosis of cancerous tissue [12]. While random forests and SVMs have proven to be popular methods for expression analysis, little work has been done to compare these methods with AdaBoost, a popular ensemble learning algorithm, across a wide array of cancer prediction tasks. Our work shows AdaBoost outperforms other approaches on binary predictions while random forests and SVMs are the best choice in multi-class predictions.

Improvements in medical imaging techniques have provided clinicians the ability to obtain detailed brain images of patients at lower costs. This increased availability of rich data opens up new avenues of research that promise better understanding of common brain ailments such as Alzheimer’s Disease and dementia. Improved data mining techniques, however, are required to leverage these new data sets to identify intermediate disease states (e.g., mild cognitive impairment) and perform early diagnosis. We propose a graphical model framework based on conditional random fields (CRFs) to mine MRI brain images. As a proof-of-concept, we apply CRFs to the problem of brain tissue segmentation. Experimental results show robust and accurate performance on tissue segmentation comparable to other state-of-the-art segmentation methods. In addition, results show that our algorithm generalizes well across data sets and is less susceptible to outliers. Our method relies on minimal prior knowledge unlike atlas-based techniques, which assume images map to a normal template. Our results show that CRFs are a promising model for tissue segmentation, as well as other MRI data mining problems such as anatomical segmentation and disease diagnosis where atlas assumptions are unreliable in abnormal brain images.

Protein X-ray crystallography — the most popular method for determining protein structures — remains a laborious process requiring a great deal of manual crystallographer effort to interpret low-quality protein images. Automating this process is critical in creating a high-throughput protein-structure determination pipeline. Previously, our group developed ACMI, a probabilistic framework for producing protein-structure models from electron-density maps produced via X-ray crystallography. ACMI uses a Markov Random Field to model the three-dimensional (3D) location of each non-hydrogen atom in a protein. Calculating the best structure in this model is intractable, so ACMI uses approximate inference methods to estimate the optimal structure. While previous results have shown ACMI to be the state-of-the-art method on this task, its approximate inference algorithm remains computationally expensive and susceptible to errors. In this work, we develop Probabilistic Ensembles in ACMI (PEA), a framework for leveraging multiple, independent runs of approximate inference to produce estimates of protein structures. Our results show statistically significant improvements in the accuracy of inference resulting in more complete and accurate protein structures. In addition, PEA provides a general framework for advanced approximate inference methods in complex problem domains.