pykeras Goto Github PK

bashutils

chroma

the AI-native open-source embedding database

complete-node-bootcamp

Starter files, final projects and FAQ for my Complete Node.js Bootcamp

dl_develop_to_deploy_workshop

4-5 days workshop from development to deploy (Deep Learning, tensorlfow, keras, mlflow, docker, ...)

fastai

Learn FastAi

frangi_filter

Compute the likeliness of an image region to vessels or ridges

multiclass_semantic_segemnet

A multiclass semantic segmentation example using Tensorflow/Keras

octconv-keras

Unofficial implementation of Octave Convolutions (OctConv) in TensorFlow / Keras.

semantic_segmentation

Semantic segmentation for medical images workshop

shape_predictor_81_face_landmarks

Custom shape predictor model trained to find 81 facial feature landmarks given any image

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.