[MICCAI'24] Official implementation of "BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection".

Patho-GAN: interpretation + medical data augmentation. Code for paper work "Explainable Diabetic Retinopathy Detection and Retinal Image Generation"

This repository holds the code framework used in the paper Reg R-CNN: Lesion Detection and Grading under Noisy Labels. It is a fork of MIC-DKFZ/medicaldetectiontoolkit with regression capabilites.

A deep learning framework for detecting lesions in CT scans from Deep Lesion dataset

DeepLesion

Official code for "DermSynth3D: Synthesis of in-the-wild Annotated Dermatology Images". A data generation pipeline for creating photorealistic in-the-wild synthetic dermatalogical data with rich multi-task annotations for various skin-analysis tasks.

LesNet (Lesion Net) is an open-source project for AI-based skin lesion detection. It aims to create a reliable tool and foster community involvement in critical AI problems. Contributions are welcome!

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

TensorFlow implementation of our paper: "Automated detection of aggressive and indolent prostate cancer on magnetic resonance imaging [Medical Physics 2021]".

Elsevier-CIBM-2024: Synergistic fusion of image-level and fine-grained disease attention for multi-label lesion detection in chest X-rays

SEU Bachelor Thesis Project of a auto-diagnosis system for fundus diseases based on deep learning

Project for UCSF 265

ProLesA-Net: a Deep learning model For Prostate Lesion Segmentation from bi-parametric MR-Images

[WACV'25] Official implementation of "PK-YOLO: Pretrained Knowledge Guided YOLO for Brain Tumor Detection in Multiplane MRI Slices".

Brain Tumour Segmentation with TrUE-Net tool - top 10 DL model in MICCAI BraTS 2020

Repository for our deep multitask paper in DOT imaging.

Rasa Gastroenterologist AI Chatbot to help doctor detect patients colon cancer lesions using Unity, Darknet Yolo, Keras CNN

[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.

Detection of cerebral microbleeds using deep learning method consisting of 2 steps: initial candidate detection and candidate discrimination using a student-teacher network.

Predict lesions using various unsupervised learning techniques on the Deeplesion dataset.