lrsoenksen Goto Github PK

followers: 22.0 following: 0.0 repos: 19.0 gists: 1.0

Name: lrsoenksen

Type: User

Company: MIT / Harvard

Bio: Biomedical innovator & entrepreneur

Twitter: lrsoenksen

Location: Cambridge, MA

Blog: https://www.linkedin.com/in/luis-ruben-soenksen-57a7972a/?locale=en_US

lrsoenksen's Projects

adafruit_python_as7262

Python script for using the Sparkfun AS7262 Visible Spectrometer with the Raspberry Pi

adafruit_python_bme280

Python Driver for the Adafruit BME280 Breakout

adafruit_python_ccs811

Python driver for CCS811 air quality sensor

adafruit_python_tlc59711

Python module for the TLC59711 16-bit 12 channel RGB LED PWM driver.

aiml-stack-jupyterlab-dockerfiles

Dockerfile to generate AI/ML Ready Docker Container with GPU support and JupyterLab

bioautomated

Automated machine learning for analyzing, interpreting, and designing biological sequences

cl_rna_synthbio

Code to reproduce Angenent-Mari, N. et al 2020. Deep Learning for RNA Synthetic Biology

conky-pro

Conky file for beautiful & functional resource display in linux desktop

gluco

Arduino Code and PCB fabrication files of GlucoPush V1

gluco_push_v1

Supplemental Material (GlucoPush: A Do-It-Yourself Add-On for Online Tracking of Personal Glucometer Use)

This repository contains the code to replicate the data processing, modeling and reporting of our Holistic AI in Medicine (HAIM) Publication in Nature Machine Intelligence (Soenksen LR, Ma Y, Zeng C et al. 2022).

lrfinder

Automatic Learning Rate Scheduled for Tensorflow-Keras

medical_sentence_tokenizer

Some of my work on splitting medical text into sentences for BERT langauge modeling training.

ml4a-guides

practical guides, tutorials, and code samples for ml4a

opendrop

Open Source Digital Microfluidics Bio Lab

rpi_zram

Script to enable ZRAM on Raspberry Pi 2 & 3

senolyticsai

Supporting code for the paper "Discovering senolytics with deep learning"

spl_ud_dl

A reported 96,480 people were diagnosed with melanoma in the United States in 2019, leading to 7230 reported deaths. Early-stage identification of suspicious pigmented lesions (SPLs) in primary care settings can lead to im- proved melanoma prognosis and a possible 20-fold reduction in treatment cost. Despite this clinical and economic value, efficient tools for SPL detection are mostly absent. To bridge this gap, we developed an SPL analysis system for wide-field images using deep convolutional neural networks (DCNNs) and applied it to a 38,283 dermatological dataset collected from 133 patients and publicly available images. These images were obtained from a variety of consumer-grade cameras (15,244 nondermoscopy) and classified by three board-certified dermatologists. Our system achieved more than 90.3% sensitivity (95% confidence interval, 90 to 90.6) and 89.9% specificity (89.6 to 90.2%) in distinguishing SPLs from nonsuspicious lesions, skin, and complex backgrounds, avoiding the need for cumbersome individual lesion imaging. We also present a new method to extract intrapatient lesion saliency (ugly duckling criteria) on the basis of DCNN features from detected lesions. This saliency ranking was validated against three board-certified dermatologists using a set of 135 individual wide-field images from 68 dermatolog- ical patients not included in the DCNN training set, exhibiting 82.96% (67.88 to 88.26%) agreement with at least one of the top three lesions in the dermatological consensus ranking. This method could allow for rapid and accurate assessments of pigmented lesion suspiciousness within a primary care visit and could enable improved patient triaging, utilization of resources, and earlier treatment of melanoma.

lrsoenksen Goto Github PK

lrsoenksen's Projects

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