Pinned Repositories
OctConv-TFKeras
Unofficial implementation of Octave Convolutions (OctConv) in TensorFlow / Keras.
BashUtils
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
Semantic_segmentation
Semantic segmentation for medical images workshop
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.
pykeras's Repositories
pykeras/BashUtils
pykeras/DL_develop_to_deploy_workshop
4-5 days workshop from development to deploy (Deep Learning, tensorlfow, keras, mlflow, docker, ...)
pykeras/Semantic_segmentation
Semantic segmentation for medical images workshop
pykeras/multiclass_semantic_segemnet
A multiclass semantic segmentation example using Tensorflow/Keras
pykeras/fastai
Learn FastAi
pykeras/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.
pykeras/complete-node-bootcamp
Starter files, final projects and FAQ for my Complete Node.js Bootcamp
pykeras/frangi_filter
Compute the likeliness of an image region to vessels or ridges
pykeras/OctConv-Keras
Unofficial implementation of Octave Convolutions (OctConv) in TensorFlow / Keras.
pykeras/shape_predictor_81_face_landmarks
Custom shape predictor model trained to find 81 facial feature landmarks given any image
pykeras/chroma
the AI-native open-source embedding database