Multi-task framework for breast cancer segmentation and classification

Breast Cancer's Ultrasound Images Dataset ( Segmentation and Classification )

This repository contains iPython notebook files that address the problem of breast cancer detection on ultrasound images. The problem of breast cancer detection is approached from several different ways- Benign vs. Malignant BUS Tumor classification(with and without ground truth tumor masks), Semantic Segmentation of Breast Tumor in BUS images, and Multitask learning based segmentation and classification of BUS tumors. The code and the results present in this repository can serve as a baseline for innovative research works in this area.

这是一个基于YOLOv8目标检测算法的乳腺癌计算机辅助诊断系统，可以实现乳腺癌影像图片的的检测、分割、分类。This is a YOLOv8-based computer-aided diagnosis system for breast cancer that enables detection, segmentation, and classification of mammography images.

Contains code for my B.Tech Project on classification and segmentation of breast cancer images

Sample codes regarding the research paper titled "A robust encoder decoder based weighted segmentation and dual staged feature fusion based meta classification for breast cancer utilizing ultrasound imaging".

ML Classification and Segmentation for Breast Cancer

Breast cancer tumor segmentation and lesion classification from ultrasound images. Developed for UCSC Biohacks 2025.

Glandular formation and morphology along with the architectural appearance of glands exhibit significant importance in the detection and prognosis of inflammatory bowel disease and colorectal cancer. The extracted glandular information from segmentation of histopathology images facilitate the pathologists to grade the aggressiveness of tumor. Manual segmentation and classification of glands is often time consuming due to large datasets from a single patient. We are presenting an algorithm that can automate the segmentation as well as classification of H and E (hematoxylin and eosin) stained colorectal cancer histopathology images. In comparison to research being conducted on cancers like prostate and breast, the literature for colorectal cancer segmentation is scarce. Inter as well as intra-gland variability and cellular heterogeneity has made this a strenuous problem. The proposed approach includes intensity-based information, morphological operations along with the Deep Convolutional Neural network (CNN) to evaluate the malignancy of tumor. This method is presented to outpace the traditional algorithms. We used transfer learning technique to train AlexNet for classification. The dataset is taken from MCCAI GlaS challenge which contains total 165 images in which 80 are benign and 85 are malignant. Our algorithm is successful in classification of malignancy with an accuracy of 90.40, Sensitivity 89% and Specificity of 91%. here is a copy of this project from a

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This project utilizes deep learning and K-Means Clustering to achieve accurate breast cancer classification from BRACS images, emphasizing automatic feature extraction and segmentation for enhanced precision.

This is a project for brain and breast cancer classification and segmentation

Breast Cancer Segmentation and Classification

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Automated Breast Cancer Imaging Attention UNet Segmentation and CNN Classification

Breast cancer type classification and segmentation using multi-task u-net model

AI-powered breast cancer detection using image enhancement, segmentation, and CNN-based classification with up to 99.67% accuracy.

A study on Multilayer Perceptron Neural Networks (MLP) for classification tasks using the Skin Segmentation and Breast Cancer datasets.

This project implements a MultiTaskUNet model using PyTorch for lesion segmentation and classification of breast ultrasound images (Normal, Benign, Malignant). It automates breast cancer detection with preprocessing, augmentation, and evaluation metrics.

Developed a robust image classification and segmentation system for breast cancer diagnosis using Convolutional Neural Networks (CNNs). Implemented transfer learning with MobileNet architecture on a comprehensive breast cancer image dataset. Leveraged OpenCV for image preprocessing and analysis, enhancing model accuracy.

Key Features: Source: Images are collected from real-world diagnostic cases at Popular Diagnostic Center, ensuring clinical relevance. Format: The dataset includes high-quality images labeled for diagnostic purposes. Applications: Suitable for training machine learning models for breast cancer detection, classification, and segmentation tasks.

A demonstration project of using pyConText to identify the patients with family history of breast cancer and/or colon cancer. It includes sentence segmentation, pyConText, document classification(rule-based) and visualization.

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Breast Cancer Segmentation and Classification using ultrasound images of breast.

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our graduation project to detect and diagnose breast cancer

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