prmsgautam's profile

Pramesh Gautam

prmsgautam

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Member for 4 years, 7 months

Activity Overview

ICIAR 2018

Challenge User

Can you develop a method for automatic detection of cancerous regions in breast cancer histology images?

CHAOS

Challenge User

In this challenge, you segment the liver in CT data, and segment liver, spleen, and kidneys in MRI data.

EAD2019

Challenge User

Endoscopic Artefact Detection (EAD) is a core problem and needed for realising robust computer-assisted tools. The EAD challenge has 3 tasks: 1) Multi-class artefact detection, 2) Region segmentation, 3) Detection generalisation.

Decathlon

Challenge User

The Medical Segmentation Decathlon challenge tests the generalisability of machine learning algorithms when applied to 10 different semantic segmentation task.

PAIP 2019

Challenge User

PAIP2019: Liver Cancer Segmentation Task 1: Liver Cancer Segmentation Task 2: Viable Tumor Burden Estimation

curious2019

Challenge User

MICCAI Challenge 2019 for Correction of Brainshift with Intra-Operative Ultrasound. Taks 1: Register pre-operative MRI to iUS before tumor resection;Taks 2: Register iUS after tumor resection to iUS before tumor resection

PatchCamelyon

Challenge User

PatchCamelyon is a new and challenging image classification dataset of 327.680 color images (96 x 96px) extracted from histopathology images of the CAMELYON16 challenge. The goal is to detect breast cancer metastasis in lymph nodes.

StructSeg2019

Challenge User

Welcome to Automatic Structure Segmentation for Radiotherapy Planning Challenge 2019. This competition is part of the MICCAI 2019 Challenge.

DigestPath2019

Challenge User

Welcome to Digestive-System Pathological Detection and Segmentation Challenge 2019. This competition is part of the MICCAI 2019 Challenge.

Lymphocyte Assessment Hackathon

Challenge User

Lymphocyte Assessment Hackathon in conjunction with the MICCAI COMPAY 2019 Workshop on Computational Pathology

HEROHE

Challenge User

Unlike previous challenges, this proposes to find an image analysis algorithm to identify HER2-positive from HER2-negative breast cancer specimens evaluating only the morphological features present on the HE slide, without the staining patterns of IHC.

EndoCV2020

Challenge User

Endoscopy computer vision challenge (EndoCV2020) introduces two core sub-themes in endoscopy: 1) artefact detection and segmentation (EAD2020) and 2) disease detection and segmentation (EDD2020).

LNDb Challenge

Challenge User

Lung cancer screening and Fleischner follow-up determination in chest CT through nodule detection, segmentation and characterization

Learn2Reg

Challenge User

Challenge on medical image registration addressing: learning from small datasets; estimating large deformations; dealing with multi-modal scans; and learning from noisy annotations

SARAS-ESAD

Challenge User

This challenge is part of Medical Imaging with Deep Learning conference, 2020. The conference is held between 6 ‑ 8 July 2020 in Montréal. The SARAS (Smart Autonomous Robotic Assistant Surgeon) EU consortium, www.saras-project.eu, is working towards replacing the assistant surgeon in MIS with two assistive robotic arms. To accomplish that, an artificial intelligence based system is required which not only can understand the complete surgical scene but also detect the actions being performed by the main surgeon. This information can later be used infer the response required from the autonomous assistant surgeon.

CT diagnosis of COVID-19

Challenge User

Coronavirus disease 2019 (COVID-19) has infected more than 1.3 million individuals all over the world and caused more than 106,000 deaths. One major hurdle in controlling the spreading of this disease is the inefficiency and shortage of medical tests. To mitigate the inefficiency and shortage of existing tests for COVID-19, we propose this competition to encourage the development of effective Deep Learning techniques to diagnose COVID-19 based on CT images. The problem we want to solve is to classify each CT image into positive COVID-19 (the image has clinical findings of COVID-19) or negative COVID-19 ( the image does not have clinical findings of COVID-19). It’s a binary classification problem based on CT images.

AutoImplant

Challenge User

the MICCAI 2020 Cranial Implant Design Challenge

PAIP 2023: TC prediction in pancreatic and colon cancer

Challenge User

Tumor cellularity prediction in pancreatic cancer (supervised learning) and colon cancer (transfer learning)