The Aim of research is to develop effective method of Lesion Segmentation from dermoscopic images. The Scope of research includes the Image pre-processing, Lesion localization and segmentation of Skin lesions (Melanoma, Nevus and Seborrheic Keratosis).

this repo's goal is an improvement in overall development capability about image processing

Deep learning models to lung lesion segmentation & classification on CT slices with pytorch.

Omni-supervised domain adversarial training for WM hyperintensity segmentation

Segmentation Guided Scoring of Pathological Lesions in Swine Through CNNs

calculate quality metrics for lesion segmentation results

[IJHCS] UTA7: a dataset of heatmaps and images resulted from computing the given abnormalities which were manually delineated by clinicians while annotating the breast cancer lesions.

Rasa breast cancer radiology AI chatbot to help doctor segment lesions using Unity, Keras Attention UNet, LinkNet, etc

Skin Lesion Segmentation

metrics for evaluating lesion segmentations

Skin lesion segmentation with a U-Net, using the dataset from ISIC challenge 2018.

Project for UCSF 265

A 2D and 3D PyTorch implementation of the Tiramisu CNN

Breast ultrasound (BUS) image segmentation using region-growing algorithm

Active learning-based interactive tool for semi-supervised image segmentation

This repository contains skin cancer lesion detection models. These are trained on a sequential and a custom ResNet model

[BSPC] The official code for "MHorUNet: High-order spatial interaction UNet for skin lesion segmentation".

DL tool for white matter hyperintensities segmentation

DeepWMH: Annotation-free white matter hyperintensity (WMH) lesion segmentation tool using deep learning.