FEDD -- Fair, Efficient, and Diverse Diffusion-based Lesion Segmentation and Malignancy Classification

Skin diseases affect millions of people worldwide, across all ethnicities. Increasing diagnosis accessibility requires fair and accurate segmentation and classification of dermatology images. However, the scarcity of annotated medical images, especially for rare diseases and underrepresented skin tones, poses a challenge to the development of fair and accurate models. In this study, we introduce a Fair, Efficient, and Diverse Diffusion-based framework for skin lesion segmentation and malignancy classification. FEDD leverages semantically meaningful feature embeddings learned through a denoising diffusion probabilistic backbone and processes them via linear probes to achieve state-of-the-art performance on Diverse Dermatology Images (DDI). We achieve an improvement in intersection over union of 0.18, 0.13, 0.06, and 0.07 while using only 5%, 10%, 15%, and 20% labeled samples, respectively. Additionally, FEDD trained on 10% of DDI demonstrates malignancy classification accuracy of 81%, 14% higher compared to the state-of-the-art. We showcase high efficiency in data-constrained scenarios while providing fair performance for diverse skin tones and rare malignancy conditions. Our newly annotated DDI segmentation masks and training code can be found on https://github.com/hectorcarrion/fedd

EIT: Earnest Insight Toolkit for Evaluating Students' Earnestness in Interactive Lecture Participation Exercises

In today's rapidly evolving educational landscape, traditional modes of passive information delivery are giving way to transformative pedagogical approaches that prioritize active student engagement. Within the context of large-scale hybrid classrooms, the challenge lies in fostering meaningful and active interaction between students and course content. This study delves into the significance of measuring students' earnestness during interactive lecture participation exercises. By analyzing students' responses to interactive lecture poll questions, establishing a clear rubric for evaluating earnestness, and conducting a comprehensive assessment, we introduce EIT (Earnest Insight Toolkit), a tool designed to assess students' engagement within interactive lecture participation exercises - particularly in the context of large-scale hybrid classrooms. Through the utilization of EIT, our objective is to equip educators with valuable means of identifying at-risk students for enhancing intervention and support strategies, as well as measuring students' levels of engagement with course content

An insight to HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) pathogenesis; evidence from high-throughput data integration and meta-analysis

Background Human T-lymphotropic virus 1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) is a progressive disease of the central nervous system that significantly affected spinal cord, nevertheless, the pathogenesis pathway and reliable biomarkers have not been well determined. This study aimed to employ high throughput meta-analysis to find major genes that are possibly involved in the pathogenesis of HAM/TSP. Results High-throughput statistical analyses identified 832, 49, and 22 differentially expressed genes for normal vs. ACs, normal vs. HAM/TSP, and ACs vs. HAM/TSP groups, respectively. The protein-protein interactions between DEGs were identified in STRING and further network analyses highlighted 24 and 6 hub genes for normal vs. HAM/TSP and ACs vs. HAM/TSP groups, respectively. Moreover, four biologically meaningful modules including 251 genes were identified for normal vs. ACs. Biological network analyses indicated the involvement of hub genes in many vital pathways like JAK-STAT signaling pathway, interferon, Interleukins, and immune pathways in the normal vs. HAM/TSP group and Metabolism of RNA, Viral mRNA Translation, Human T cell leukemia virus 1 infection, and Cell cycle in the normal vs. ACs group. Moreover, three major genes including STAT1, TAP1, and PSMB8 were identified by network analysis. Real-time PCR revealed the meaningful down-regulation of STAT1 in HAM/TSP samples than AC and normal samples (P = 0.01 and P = 0.02, respectively), up-regulation of PSMB8 in HAM/TSP samples than AC and normal samples (P = 0.04 and P = 0.01, respectively), and down-regulation of TAP1 in HAM/TSP samples than those in AC and normal samples (P = 0.008 and P = 0.02, respectively). No significant difference was found among three groups in terms of the percentage of T helper and cytotoxic T lymphocytes (P = 0.55 and P = 0.12). Conclusions High-throughput data integration disclosed novel hub genes involved in important pathways in virus infection and immune systems. The comprehensive studies are needed to improve our knowledge about the pathogenesis pathways and also biomarkers of complex diseases.Peer reviewe

Alcohol Withdrawal Syndrome Assessment based on Tremor Time-Frequency Analysis

In this thesis we established signal processing techniques to objectively evaluate the severity of Alcohol Withdrawal (AW) tremors. Many medical protocols were used pre- viously to help the physicians in assessing the severity of alcohol withdrawal, but those techniques were subjective and relied on the experience level of the physicians. The key objective throughout this thesis is investigating the logarithmic nature of the energy emitted from tremor signals. We are able to use the energy from tremor recordings in the frequency range of [5, 15] Hz to train a logarithmic model to estimate the severity of tremors. The next step in validating the effectiveness of the logarithmic model is the validation of the methodology in a clinical setting. The model is being validated in the emergency department for a 10-month period. During this period, each of the AW patients have been evaluated by one nurse and have been videotaped while acquiring the signal. The model provides the severity score in realtime after recording the signal. Our model is validated by comparing the score given by the model and the consensus severity rating from a panel of three expert physicians after viewing the videos. We concluded that there is a reliable agreement (kappa 0.92, 95% CI: 0.86, 0.99) between the score given by the model and the rating from our panel. Further contributions of this thesis include an investigation of the features of AW tremors in classifying factitious vs. real tremors, based on the mean peak frequency and band-limited energy. Additionally we evaluate the differences between AW tremors in both hands and observe that by averaging the tremor ratings of each hand, a more ac- curate result can be obtained compared to taking either of the individual hand ratings. Lastly, to remove the noise from the tremor signal, an Empirical Mode Decomposition (EMD) algorithm was utilized. EMD decomposes the signal into different Intrinsic Mode Functions (IMFs) and IMFs with the peak frequency in the frequency range of the tremor will be a part of the reconstructed signal. Using this technique, we successfully enhanced the accuracy of our logarithmic model.Ph.D

Multi-modal Heart-Beat Estimation on an iPhone

Current generation smartphone video cameras and microphones enable photoplethysmography (PPG) and phonocardiography (PCG) acquisition. In this thesis, I utilized the iPhone microphone and camera to measure heart rate. We developed a heart rate measurement system using triple sensing mechanisms (finger and face color changes and heart sound measurement) all on the iPhone. The three proposed measurement systems each provide an independent heart rate estimate, as well as a combined estimation based on the fusion of the individual sensors.The proposed algorithm estimates the heart rate by (1) heart pulse analysis to compute the heart rate of the user using our version of the EMD algorithm which is used in advanced biomedical signal processing, (2) assessing the quality of the PPG and PCG waveforms using the Support Vector Machine (SVM) classifier,(3) concisely combining heart rate information from the three different modalities based on the assessed quality of the waveforms.M.A.S

Context-Aware Analysis of Group Submissions for Group Anomaly Detection and Performance Prediction

Learning exercises that activate students’ additional cognitive understanding of course concepts facilitate contextualizing the content knowledge and developing higher-order thinking and problem-solving skills. Student-generated instructional materials such as course summaries and problem sets are amongst the instructional strategies that reflect active learning and constructivist philosophy. The contributions of this work are twofold: 1) We introduce a practical implementation of inside-outside learning strategy in an undergraduate deep learning course and will share our experiences in incorporating student-generated instructional materials learning strategy in course design, and 2) We develop a context-aware deep learning framework to draw insights from the student-generated materials for (i) Detecting anomalies in group activities and (ii) Predicting the median quiz performance of students in each group. This work opens up an avenue for effectively implementing a constructivism learning strategy in large-scale and online courses to build a sense of community between learners while providing an automated tool for instructors to identify at-risk groups

Lessons Learned from Teaching Machine Learning and Natural Language Processing to High School Students

This paper describes an experience in teaching Machine Learning (ML) and Natural Language Processing (NLP) to a group of high school students over an intense one-month period. In this work, we provide an outline of an AI course curriculum we designed for high school students and then evaluate its effectiveness by analyzing student's feedback and student outcomes. After closely observing students, evaluating their responses to our surveys, and analyzing their contribution to the course project, we identified some possible impediments in teaching AI to high school students and propose some measures to avoid them. These measures include employing a combination of objectivist and constructivist pedagogies, reviewing/introducing basic programming concepts at the beginning of the course, and addressing gender discrepancies throughout the course

Quality and quantity of bone at intraoral graft donor sites in type 2 diabetic patients versus healthy controls: A cone-beam computed tomography study

Objectives: This study aimed to compare the quality and quantity of bone at intraoral autogenous graft donor sites in type II diabetes mellitus (DM) patients versus healthy controls using cone-beam computed tomography (CBCT). Materials and methods: This case-control study was conducted on CBCT scans of 50 DM patients and 50 healthy controls between 20-70 years. Maximum height, width, length, and volume of harvestable bone at the symphysis, ramus, palate, and tuberosity were measured bilaterally. The Hounsfield unit (HU) was also calculated to assess bone quality. The two groups were compared regarding the quality and quantity of harvestable bone using an independent t-test. The effect of confounders was analyzed by the regression model (alpha = 0.05). Results: DM patients had significantly lower harvestable bone volume at the symphysis, ramus, and tuberosity than healthy controls (p < 0.001) but this difference was not significant at the palate (p = 0.957). Also, bone quality was significantly lower at the symphysis, ramus, palate, and tuberosity in DM patients (p < 0.001). Conclusion: Diabetic patients had significantly lower bone quality and quantity at intraoral graft donor sites than healthy controls. Mandibular symphysis had higher bone volume and density than ramus, palate, and tuberosity for graft harvesting in diabetic patients